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Master Thesis ǀ Tesis de Maestría submitted within the UNIGIS MSc programme presentada para el Programa UNIGIS MSc at/en Interfaculty Department of Geoinformatics- Z_GIS Departamento de Geomática – Z_GIS University of Salzburg ǀ Universidad de Salzburg
Comparison of three image classification approaches for land use and land cover (LULC) identification A case study in the Agricultural area of San Cristobal Island, Galapagos Islands, Ecuador by/por
María Carolina Sampedro Jara 1524670 A thesis submitted in partial fulfilment of the requirements of the degree of Master of Science (Geographical Information Science & Systems) – MSc (GIS) Advisor ǀ Supervisor: Diana María Contreras Mojica
Quito - Ecuador, October 24th 2016
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Compromiso de Ciencia
Por medio del presente documento, incluyendo mi firma personal certifico y aseguro que mi tesis es completamente el resultado de mi propio trabajo. He citado todas las fuentes que he usado en mi tesis y en todos los casos he indicado su origen.
Quito, 24 de octubre de 2016
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Agradecimientos Agradezco a la persona que siempre me impulso a ser un mejor ser humano y profesional, que siempre me apoyó con cariño y buenos consejos, quien ahora desde el cielo sigue guiando mis decisiones. Agradezco a mi papá Galo Sampedro quien se merece este esfuerzo y muchos más. A mi esposo Esteban quien ha sabido apoyarme a lo largo de esta larga jornada, inspirándome siempre a dar lo mejor de mi, y a esforzarme para conseguir las metas que me proponga. También agradezco por su compañía a mi familia Susana, Ana Cris y Nina, quienes son mis pilares. Un especial agradecimiento a Carlos Mena, quien ha sido extraordinario como persona y como profesional, quien me ha enseñado y me ha permitido trabajar y crecer en el campo de la Geografía que tanto nos gusta.
5 Abstract The application of Remote Sensing techniques in threatened ecosystems such as the Galapagos Islands, have shown to be a powerful tool for decision making. Specifically in the case of San Cristobal Island it will allow accurate mapping and modeling techniques at relative low costs for battling against invasive species such as Guava and Wax apple. This research is meant to evaluate the performance of three classification techniques for land cover mapping in the agricultural area of San Cristobal Island in the Galapagos: (a) pixel-based hybrid (supervised/non-supervised classification), (b) the principal components pixel-based hybrid, and (c) object-oriented image hybrid classifications. Also an evaluation of 3 parametric classification algorithms (Maximum Likelihood, Mahalanobis Distance and Minimum Distances) for classification technique was performed.
The goal was to compare and identify the best approach for
determining LULC with an important approach towards invasive species such as Guava in the highland territory. The results for both pixel-based approaches are superior than the object based approach, nevertheless it was evident that the principal component classifications tend to mix signatures responses, and did not show the same discrimination ability. Per-pixel/hybrid classification with Maximum Likelihood and Mahalanobis Distance perform a superior kappa index of 0.8640 and 0.8610 respectively, demonstrating to be more sensitive towards identifying invasive species such as Guava and Wax Apple fields.
6 Resumen La aplicación de técnicas de teledetección en los ecosistemas amenazados, como las Islas Galápagos, han demostrado ser una poderosa herramienta para la toma de decisiones. Específicamente en el caso de la isla de San Cristóbal, los sensores remotos permiten la elaboración de cartografía y modelado espacial precisos a costos relativamente bajos, información que puede ser usada como un insumo importante para luchar contra las especies invasoras tales como la Guayaba y la Pomarosa. Esta investigación está destinada a evaluar el desempeño de tres técnicas de clasificación para el mapeo de uso y cobertura del suelo en la zona agrícola de la Isla San Cristóbal en las Galápagos: (a) Per-Pixel , (b) Per-Pixel/Componentes Principales, y (c) Clasificaciones basada en objeto. También se testeó una evaluación de 3 algoritmos de clasificación paramétrica (Maximum Likelihood, Mahalanobis Distances y Minimum Distance). El objetivo fue comparar e identificar el mejor método para determinar LULC con un enfoque especial en la identificación de especies invasoras en el área de estudio. Los resultados de ambos enfoques basados en píxeles son superiores que el enfoque basado en objetos, sin embargo, era evidente que el método de Componentes Principales tiende a mezclar las respuestas espectrales, y no mostró la misma capacidad de discriminación. La clasificación per-píxel con Maximum Likelihood y Mahalanobis Distance obtuvó un índice kappa superior de 0.8640 y 0.8610 respectivamente, además de demostrar ser más sensible a la identificación de las especies invasoras, tales como la Guayaba y la Pomarosa.
7 INDEX
1.
Introduction....................................................................................................12
1.1.
Background.....................................................................................................12
1.2.
Research Objectives.........................................................................................14
1.2.1.
General Objective.....................................................................................................................14
1.2.2.
Specific Objectives...................................................................................................................14
1.3.
Research Questions.........................................................................................14
1.4.
Justification.....................................................................................................15
1.5.
Research Scope................................................................................................16
2.
Theorical Framework.......................................................................................17
2.1.
Galapagos Islands Ecosystem...........................................................................17
2.2.
Remote Sensing...............................................................................................17
2.2.1.
Multispectral Classification......................................................................................................19
a)
Training: Supervised and Unsupervised...........................................................................................19
b)
Unsupervised Training - Clustering...................................................................................................21
c)
Evaluating signatures........................................................................................................................22
d)
Classification decision rule: Parametric and Non-parametric..........................................................23
2.2.2.
Enhancement: Principal components.....................................................................................27
2.2.3.
Per pixel - Object based approach...........................................................................................28
2.2.4.
Accuracy Assessment...............................................................................................................29
3.
Materials & Methods......................................................................................32
3.1.
Study Area.......................................................................................................32
3.2.
Characterization of the Area............................................................................34
3.3.
Data Sources....................................................................................................35
3.3.1.
Ground Data.............................................................................................................................35
3.3.2.
Secondary Data........................................................................................................................40
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3.4.
Satellite Data............................................................................................................................40
Methods..........................................................................................................40
3.4.1.
Hybrid Supervised - Unsupervised Classification.....................................................................42
3.4.2.
Principal Components with Hybrid Supervised - Unsupervised Classification........................43
3.4.3.
Object Based Hybrid Supervised - Unsupervised Classification...............................................44
3.4.4.
Validation Method...................................................................................................................45
4.
Results............................................................................................................47
4.1.
Principal Component Selection........................................................................47
4.2.
Object Based - Segmentation Image.................................................................47
4.3.
Classification Comparison................................................................................49
4.4.
Accuracy Assessment.......................................................................................53
5.
Discussion.......................................................................................................59
6.
Conclusion.......................................................................................................65
7.
References.......................................................................................................67
8.
Annex..............................................................................................................75
8.1.
Annex 1. Classification results for Maximum Likelihood, Mahalanobis Distance
and Minimum Likelihood for each approach (Pixel Based, Principal Component - Pixel Based, Object Based Classifications)...........................................................................75 8.2.
Annex 2. Confusion matrix for each classification............................................77
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MAPS Map 1. Location of Study Area....................................................................................33 Map 2. Distribution of the field points within the study area......................................37 Map 3. Ground control data........................................................................................39 Map 4. Classification results for Maximum Likelihood for each approach...................52 Map 5. Subset area for Invasive Species classification review for each of the 9 results. ...................................................................................................................................58 Mapa 6. Classification results for Maximum Likelihood, Mahalanobis Distance and Minimum Likelihood for each approach (Pixel Based, Principal Component - Pixel Based, Object Based Classifications)............................................................................76
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FIGURES Figure 1.
Iterative Self-Organizing Data Analysis Technique – ISODATA process
representation. (Erdas Inc, 1999).................................................................................22 Figure 2. Minimum Distance representation...............................................................24 Figure 3. Workflow.....................................................................................................41 Figure 4. Feature extraction process. (Source: ENVI EX, Feature Extraction Tutorial). .44 Figure 5. Image segmentation result - Object based image........................................48 Figure 6. Area values (ha.) for each class, within each classification method..............50 Figure 7. Standard deviation of the area (ha) results for the 3 classification approaches ...................................................................................................................................51 Figure 8. Kappa results................................................................................................54 Figure 9. kappa results for each classification method.................................................62
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TABLES Table 1. Ground data collected in the field..................................................................36 Table 2. Ground control data technical information.....................................................38 Table 3. Hybrid Supervised – Unsupervised Classification steps...................................42 Table 4. Parameter Settings for the Feature Extraction...............................................45 Table 5. Spectral Analysis of the Principal Components..............................................47 Table 6. Area (ha) of each class for the three approaches...........................................49 Table 7. Accuracy assessment results for the pixel based, principal component - pixel based and object based classifications approaches.....................................................53 Table 8. Marginal Homogeneity significance values...................................................55 Table 9. User´s and Producer´s accuracy for Guava and Wax Apple Fields...................56
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1. Introduction 1.1.
Background
The Galapagos Islands are extremely vulnerable to many forms of human–related pressures, which have threatened the ecological integrity of the terrestrial and marine ecosystems (Gonzalez, Montes, Rodriguez, & Tapia, 2008). Some of the most common driving factors that act upon territory dynamics and may also generate unsustainable development are, extensive increase of traffic, tourism and the resident population, subsequently the increment of demand for goods and services, as well as the arrival of invasive species. These issues seems to be repeated over and over again in the World Cultural and Natural Heritage monitoring mission reports on the Galapagos Islands (UICN, 2007, 2010a, 2016). Therefore, terrestrial and marine ecosystems, especially in the inhabited islands, are degrading at an alarming rate raising national and international concerns. In order to manage these driving factors, legal regulations and management plans have been put together by a joint effort of the Galapagos National Park administration and the local and national authorities, as is the case of the new Special Law “Ley Orgánica de Regimen Especial de la Provincia de Galápagos” (Registro Oficial No. 520, 2015). Also private institutions, foundations and NGOs efforts have become a key element, whom with international funds have manage to launch several campaigns and projects to support conservation on the islands (González et al., 2008). Just to mention, more than 50 million dollars were invested by governmental and international funds for the project “Control the Invasive Species in the Galapagos Archipelago”, which extended between the 2002-2011 (Coello & Saunders, 2011).
Afterwards, the
Ecuadorian government have just invested $16.704.405 dollars for the period of 20132017, in projects of control and eradication of invasive species (Ministerio del Ambiente, 2013). Nevertheless, in spite of those efforts, the Galapagos was added to the list of World Heritage at Risk in 2007, and since 2010 that was removed from the list, it is still under continuous monitoring (UICN, 2010b).
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At the same time that millions are been invested in conservation efforts, on the other side of the wall the industry of tourism has been growing at an average rate of 9.6 (Ministerio del Ambiente, 2013). Thus, it became the most important driver of the Galapagos economy and its rapid growth is currently the main driver of change in the islands (Taylor, 2006; Grenier, 2000). Such economic growth has boosted immigration from the mainland and has further increased coastal settlements and transformed them into big centers of economic activities. Tourism activities have generated abandonment of agriculture and cattle ranching which occupied the human-use areas on the humid highlands. Thus, the proportion of rural population in Galápagos decreased from 42% in 1974 to just 17% in 2010 (INEC, 2010). In consequence these areas are likely to become centers of establishment and propagation of invasive species such as Guava and blackberry (González et al., 2008) which will easily invade neighboring properties including the National Park restricted area. Moreover, the abandonment of agricultural lands and the establishment of new settlements in areas close to the sea has proven to increase the import of supplies from mainland which in turn are the most important source of the arrival of more invasive species (Cremers, 2002; González et al., 2008). In this sense, it is essential for Governmental authorities and other stakeholders to access to reliable information regarding land use and land cover dynamics. Accurate and up to date land use and land cover change information will allow the monitoring and assessment of spatial processes, which emerge from the highland´s ecological and social process.
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1.2.
Research Objectives
1.2.1. General Objective Assess Pixel-based, Principal Components and Object based image classification techniques for land use and land cover identification in sensitive and threatened ecosystems of the Galapagos Islands.
1.2.2. Specific Objectives
Perform three classification approaches based on the same image for land use and land cover identification of sensitive and threatened ecosystems at the Galapagos Islands: Hybrid image classification, principal components -
hybrid image classification, and objet based - hybrid classification. Evaluate each classification result using an accuracy assessment matrix to
identify the most accurate classification approach. Identify the sensibility of these three methods to the identification of invasive species, such as Guava and Wax Apple fields, among other land use and land cover classes, such as crops, abandoned lands, coffee cultivation, pastures, bare soils, natural vegetation and infrastructure. 1.3.
Research Questions
Which classification approach (Hybrid image classification, Principal components hybrid image classification, and object based - hybrid classification) represents the
ecosystems complexity at the Galapagos Islands in the most accurate way? Which classification approach is most sensitive towards identifying invasive species such as Guava and Wax Apple fields at the Galapagos Islands?
1.4.
Justification
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Remote sensing image classification is a commonly used method to obtain land use and land cover (LULC) information from satellite images (Yan, Maathuis, Xiangmin, & Van Dijk, 2006). LULC analysis is essential for understanding global environmental transformation processes (Srivastava, Han, Rico-Ramirez, Bray, & Islam, 2012), due the ability to predict LULC change depends on our ability to understand the present and future drivers of LULC change (Munroe & Muller, 2007; P. Srivastava, Gupta, & Mukherjee, 2012; Srivastava et al., 2012). Improved understanding of historical LULC change patterns provides better means to project future trends (Sharma, Pandey, & Nathawat, 2012; Srivastava, Mukherjee, & Gupta, 2010). Image classification transforms satellite images into a usable geographic product (Wilkinson, Naeth, & Schmiegelow, 2005). The method is based on sorting the image’s spectral information into a finite number of individual classes based on their data file values. Nevertheless, several methods that can be used to perform LULC classifications. Image classification approaches can be grouped in supervised and unsupervised, parametric and non-parametric, hard and soft (fuzzy), or per-pixel, sub-pixel and object based classifications (Lu & Weng, 2007). Identifying the most appropriate approach should be based on the characteristics of each specific situation (Lu & Weng, 2007) given that each classification algorithm has advantages and disadvantages.
For instance, the per-pixel approach is the most
commonly used but presents accuracy problems caused by mixed pixels, while the object based classification is mostly used in fine spatial resolution data but may allow to identify object boundaries in a more accurate way. Same pro and cons may be identified in supervised and unsupervised classifications approaches. In this sense, a comparative study of different classifiers should be conducted in order to find the best classification method for a specific study (Pal & Mather, 2004, 2003; South, Qi, & Lusch, 2004).
1.5.
Research Scope
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This research is meant to evaluate the performance of three classification techniques for land cover mapping in the agricultural area of San Cristobal Island in the Galapagos: (a) pixel-based hybrid (supervised/non-supervised classification), (b) the principal components pixel-based hybrid, and (c) object-oriented image hybrid classifications, as well as the evaluation of 3 parametric classification algorithms (Maximum Likelihood, Mahalnobis Distance and Minimum Distances), for classification technique. In order to assess these techniques, nine land use and land cover classes were obtained: crops, abandoned lands, coffee cultivation, wax apple fields, guava, pastures, bare soils, natural vegetation and infrastructure. The goal of performing the mentioned methodological comparison is to identify the best approach when determining invasive species such as guava in the highland territory.
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2.
Theorical Framework 2.1.
Galapagos Islands Ecosystem
The Galapagos Archipelago is formed by 13 islands greater than 10 km², 6 smaller islands and 40 islets which have official names, with a total area of 8000 km² (Jackson, 2007). The nearest land, mainland Ecuador, is some 960 km to the east. The Galapagos are oceanic islands which have emerged from volcanic activity, as they are located in what is known as a “hot spot”. Consequently, none of the island has never been part from a continental area. In these sense, the ecosystems of the islands are isolated systems, and communities have particular characteristics and high levels of endemism due to their natural evolution in a context of remoteness and low level or disturbance (Kier et al., 2009; Steinbauer, Otto, Naranjo-Cigala, Beierkuhnlein, & Fernandez-Palacios, 2012). Thus, oceanic islands in general and the Galapagos explicitly, are considered as a highly sensitive type of ecosystems. Despite such specific characteristics, when humans first arrive to an island, their main problem is the adaptation of the natural environment to one that provides the necessary resources for surviving, especially agricultural interventions (Fernandes, Guiomar, & Gil, 2015). These resources involved, not only products for self-consumption, but also staples for exportation. Consequently, islands become threatened ecosystems and have to deal, among other, with introduction of new species with unexpected invasive behavior also affected the size of native population, and therefore, their viability, compromising specific ecological niches of other species and communities (Gil, Lobo, Abadi, Silva, & Calado, 2013; Lourenco, Medeiros, Gil, & Silva, 2011). 2.2.
Remote Sensing
Remote sensing refers to the technique of acquiring images of the Earth's surface from aerial or space sensors and their subsequent processing and interpretation (Chuvieco, 2010). Remote sensing data records the spectral properties of surface materials (Cihlar &
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Jasen, 2001) based on the capture of radiant energy known as electromagnetic radiation and which systematization is done according to different wavelengths or frequencies and is called electromagnetic spectrum. Specialized techniques further allow the interpretation of the data (Chuvieco, 2010). Remote sensing has been an important component of earth science applications ranging from rural and urban planning to monitoring change of natural landscapes and a wide range of gray between both (Prenzel & Treitz, 2003). Information derived from remote sensing data has often been used to assist in the formulation of policies and provide insight into natural patterns and multi-temporal trends (Treitz & Rogan, 2004). In 1972, with the launch of the first satellite, accessibility to remote sensing technologies became a breaking point in the different geographic fields such as land use and land cover science (LULC). There has been an evolution in the way in which technology and analysis techniques are used to map LULC data at local, landscape, regional and continental scales. Due remote sensing provides digital data at scales of observation that meet different mapping requirements which fulfill different objectives related with anthropogenic and natural phenomenon. Consequently, remote sensing of LULC is a diverse area of study and application, with different meanings to different users and practitioners (Treitz & Rogan, 2004). According to several authors, land cover refers to the biophysical attributes that materials on the surface of a given-parcel of land have (e.g. grass, concrete, tarmac, water). While land use is defined as the human purpose or intended use of these attributes which are based on the activities that take place on or makes use of that land (e.g. residential, commercial, industrial) (Lambin et al., 2001; Barnsley, Moller-Jensen, & Barr, 2001). In this sense, land use can be seen as having an abstract concept because it is made up of a mix of social, cultural, economic and policy factors, which have little physical importance with respect to reflectance properties, and thus has a limited relationship to remote
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sensing, while the remote sensing data is more closely related to land cover (Cihlar and Jasen, 2001). The techniques that are dealt with in this study are related to the accuracy in the identification of land cover with the use of multispectral image classification. Since classifying satellite images and extracting meaningful information in an efficient way without compromising the accuracy has remained a challenge, and despite the fact that many research papers have been published in the field, there is still a lack of comparative studies on LULC classification tools in order to choose the best one among them (Srivastava et al. 2012). Several approaches of multispectral classification are being used, as in a broad manner image classification approaches can be grouped as supervised and unsupervised, parametric and non-parametric, or per-pixel, sub-pixel and object based classifications (Lu & Weng, 2007). 2.2.1. Multispectral Classification Multispectral image classification transforms satellite image to a usable geographic product (Wilkinson et al., 2005). The basic idea is to find meaningful patterns in data (spectral image data). It is based on sorting the spectral information of the image into a finite number of individual classes using statistics and mathematical calculations which are derived from the spectral characteristics of all the pixels in an image. The final goal is that each pixel is assigned to the class that corresponds to certain criteria (Erdas Inc, 1999). The classification process is broken down into two parts: training and classifying. a) Training: Supervised and Unsupervised Training is the process of defining the criteria by which defined patterns should be recognized, for doing so, the computer system is the one which is trained to recognize patterns in the data (Hord, 1982). Image classification training can be performed using either supervised or unsupervised approaches.
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The supervised classification requires prior knowledge of the ground cover in the study area and is therefore, a more intuitive method for land-cover change mapping (Rogan & Chen, 2004). To do this, land cover classes have to be defined through sufficient reference data available to be used as training samples. The signatures generated from the training samples are then used to train the classifier to categorize the spectral data into a thematic map. The general idea is that by identifying patterns, you can instruct the computer system to identify pixels with similar characteristics. If the classification is accurate, the resulting classes represent the categories within the data that the analyst initially identified (Erdas Inc, 1999). Supervised classification works better usually when few classes are being identified and when the training sites can be verified with ground truth data. On the other hand, unsupervised classification is more computer automated.
The
general principle is that an algorithm is chosen to take a remotely sensed image data set and find a pre-specified number of statistical clusters in measurement space (Schowengerdt, 1997) through the search of statistical patterns that are inherent in the data. These patterns do not necessarily correspond to direct meaningful characteristics of the scene (such as continuity), as they are simply clusters of pixels with similar spectral characteristics (Erdas Inc, 1999). The next step is to assign these clusters to classes of land cover and land use. The analyst is responsible for labeling and merging the spectral classes into meaningful classes.
This method can be used without having prior
knowledge of the ground cover on the study site. Unsupervised classification approach works better when the goal is to determine the spectral distinctions that are inherent in the data. The classes can be interpreted and defined later by the analyst. Classifiers, supervised and unsupervised have strengths and limitations (Alajlan, Bazi, Melgani, & Yager, 2012). The training process can also use a combination of supervised and unsupervised classifications (hybrid classification) which can yield optimum results especially with large data sets (Erdas Inc, 1999). This mixture of techniques allow to capture different mixed spatial and spectral classification schemes which preserve
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natural variability of the landscape for pre-set LULC classes to be characterized (Messina, Crews-Meyer, & Walsh, 2000), and also improves the accuracy of the classification with respect to standard spectral classification methods. In these sense, the unsupervised classification generates a basic set of classes and the supervised classification is used for further definition of the classes. Previous research has indicated that the integration of two or more training processes provides improved classification accuracy compared to the use of a single classifier (Alajlan et al., 2012; G. Huang, Song, Gupta, & Wu, 2014; Lu & Weng, 2007; Mohammadiha, 2013). Nevertheless, it is critical to develop suitable rules to combine the classification results from different classifiers. b) Unsupervised Training - Clustering Unsupervised Training is a statistic process focus on identify patterns that are inherent in the data. These patterns are groups of pixels with similar spectral characteristics, that´s why unsupervised training is also called clustering, as it is based on the natural grouping of pixels in image data based on its spectral information (Erdas Inc, 1999). This process uses a clustering algorithm, which is applied to all or most of the pixels of the input image. The result of this process of training is a set of signatures that defines a cluster or class, which will be used with a decision rule to assign the pixels in the image file to a class. One of the most used clustering methods is the Iterative Self-Organizing Data Analysis Technique – ISODATA algorithm, which bases its analysis on spectral distance, and iteratively classifies the pixels, redefines the criteria for each class, and classifies again, so that the spectral distance patterns in the data gradually emerge (Erdas Inc, 1999). ISODATA algorithm on its first iteration calculates the means of N clusters in a arbitrarily way. After each iteration, a new mean for each cluster is calculated, based on the spectral locations of the pixels in the cluster (Erdas Inc, 1999). Then, these new means are used for defining clusters in the next iteration, and so on until little change between iterations (Swain, 1973). This process is showed on Figure 1, which describes: First,
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clusters are arbitrarily define.
Second, the distance of the pixels spectral location
towards the mean of each cluster is measured, and the pixel is assigned to a class. Third, the process repeat again, for every iteration, and the mean of each cluster or class is recalculated.
Figure 1. Iterative Self-Organizing Data Analysis Technique – ISODATA process representation. (Erdas Inc, 1999).
The parameters considered for this method are:
# of Classes, which refers to the maximum number of clusters or classes to be
considered in the process Maximum Iterations, means the maximum number of iterations to be
performed Convergence threshold, which refers to the maximum percentage of pixels whose class values are allowed to be unchanged between iterations.
c) Evaluating signatures Once the signatures are created, they can be evaluated, deleted, renamed, and merged with signatures from other files. In this sense, there are tests which determine if the signature data is a true representation from the class which has been assign. Just to
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mention, some of the evaluation tests available: alarm, ellipse, contingency matrix, separability and statistics and histograms. The separability is a statistical method, which refers to the statistical measure of the distance between two signatures, and determines band subsets that maximize the classification. The general idea is that if the distance between two samples is not significant for any pair of bands, then they may not be distinct enough to produce a successful classification. There are three algorithms or methods to calculate the separability: divergence, transformed divergence, Jeffries-Matusita Distance.
All of these formulas take into
account the covariances of the signatures in the bands being compared, as well as the mean vectors of the signatures. The transformed divergence gives an exponentially decreasing weight to increasing distances between the classes (Erdas Inc, 1999). The range of the scale values is from 0 to 2000, the highest the value the most separated the class. As a general rule, if the result is greater than 1900 the classes can be separated, between 1700-1900 the separation is fairly good, and below 1700 the separation is poor (Jensen, 1996).
d) Classification decision rule: Parametric and Non-parametric The classification decision rule is a mathematical algorithm used for assigning pixels into distinct class values. When a parametric decision rule is used, a Gaussian distribution is assumed because this method is based on statistical parameters such as the mean and covariance matrix. In the case of using a parametric decision rule every pixel is assigned to a class since the parametric decision space is continuous (Kloer, 1994).
The
nonparametric decision rule is not based on statistical analysis and is hence independent of the properties of the data.
A nonparametric classifier uses a set of nonparametric
signatures to assign pixels to a class based on their location either inside or outside the
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area in the feature space image (Erdas Inc, 1999). Not every pixel may be assigned to a class. Common examples of parametric classifiers are: Minimum distance, Mahalanobis Distances, and Maximum Likelihood. The Minimum Distance classifier is used to classify unknown image data to classes, which minimize the distance between the image data and the class in multi-feature space. This algorithm calculates the spectral distance between the measurement vector for the candidate pixel and the mean vector for each signature (Erdas Inc, 1999). As is shown in figure 2, spectral distance is illustrated by the lines from the candidate pixel to the means of the three signatures. The candidate pixel is assigned to the class with the closest mean (Erdas Inc, 1999)
Figure 2. Minimum Distance representation.
The equation used for classification by spectral distance is based on the equation for Euclidean distance. This equation demonstrates that when spectral distance is computed for all possible values of c (all possible classes), the class of the candidate pixel is assigned to the class for which SD is the lowest.
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Source: (Swain & Davis, 1978) Where: n = number of bands (dimensions) i = a particular band c = a particular class Xₓᵥᵢ = data file value of pixel x, y in band i µcᵢ = mean of data file values in band i for the sample for class c SDxyc = spectral distance form pixel x, y to the mean of class c
Mahalanobis distance is very similar to minimum distances, but in this case the covariance matrix is used in the equation, which determine
clusters that are highly
varied and lead them to similarly varied classes, and vice versa. The equation for the Mahalanobis distance classifier shows that the pixel is assigned to the class c, for which D is the lowest.
Where: D = Mahalanobis distance c = a particular class X = the measurment vector of the candidate pixel Mc = the mean vector of the signature of class c Covc = the covariance matrix of the pixels in the signature of class c Covc¯¹ = inverse of Covc T = transposition function
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The Maximum Likelihood classifier is one of the most popular methods of classification in remote sensing in which a pixel with the maximum likelihood is classified into the corresponding class. It is based on the probability that a pixel belongs to a particular class, considering equal probabilities for all classes. The maximum likelihood equation assumes that the input bands have normal distribution. The equation for maximum likelihood/Bayesian classifier shows that the pixel is assigned to the class c, for which D is the lowest.
Where: D = weighted distance (likelihood) c = a particular class X = the measurement vector of the candidate pixel Mc = the mean vector of the sample of class c ac = percent probability that ay candidate pixel is a member of class c Covc = the covariance matrix of the pixels in the sample of class c |Covc| = determinant of Covc Covc¯š = inverse of Covc ln = natural logarithm function T = transposition function
In general, all three parametric classifiers rely heavily on a normal distribution of the data in each input band, but only the Mahalanobis Distance and Maximum Likelihood takes the variability of classes into account so both may need a longer time to compute. Also, there may be some pixels which can be misclassified given that the algorithms assign every pixel to a class even if the distance or likelihood are not optimum. The maximum likelihood classifier is considered for the most accurate classifiers and is the most commonly used in research.
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2.2.2. Enhancement: Principal components In some cases, and for optimum results, enhancement of the images can be performed in the classification process. Multispectral remote sensing data exhibit high inter-band correlation (Fung & LeDrew, 1987) which means that there is a certain degree of redundancy of information that may lead to high cost for classification if all the data is used (Li & Yeh, 1998). To be able to manage these scenarios, a compression of the data into fewer dimensions can be done. For doing so, the most commonly used technique is the principal components analysis (PCA). Principal components analysis (PCA) can be interpreted as a spectral enhancement technique that compresses the spectral bands through statistics algorithms and extracts new bands of data eliminating the noise and the redundant information (Ceballos & Bottino, 1997; Erdas Inc, 1999). The idea is to simplify data processing of satellite multispectral imagery (Richards, 1996).
The expected result of PCA is to change pixel
definition from M-channel sets of numbers (counts) to K-principal-components (PC) sets without significant loss of information and so allowing to save computing time (Ceballos & Bottino, 1997). Once the principal components (PC) of an image are extracted, it is noticed that the first principal component stores the maximum contents of the variance of the original data set. The second PC describes the large amount of the variance in the data that was not described by the first PC and so on (Tayor, 1977). Although an “n� number of principal components may be acquired in the analysis, only the first few PC account for a high proportion of the variance in the data. In some cases, almost 100% of the variance can be captured by these few components (Li & Yeh, 1998); (Peiman, 2011) . Several studies have shown that in vegetated areas, a simple cluster analysis based on two PCs shows the same discrimination ability as a six -waveband information (Ceballos & Bottino, 1997).
28
Nevertheless, some studies have encountered that there may be cases in which the discarded PCs may be necessary for proper discrimination. In some cases, even all PC may be needed (Li & Yeh, 1998). Further, it has been noticed that an eigenvalue does not necessarily assess the contribution of that PC to the variance of a given variable but of the whole system of variables and therefore, high-eigenvalued components could fail to be retained as proper variables (Lark, 1995). In this sense, it is important to acknowledge the data that is being evaluated in this process because the analysis may provide relevant information about a system data set prior to the use of any automatic discrimination method. 2.2.3. Per pixel - Object based approach Spectral classification can be executed on a per-pixel or object oriented basis. The perpixel approach are the traditional classifiers which develop a signature by combining the spectra of all training-set pixels from a given feature. Thus, the resulting signature contains the contributions of all materials present in the training-set pixels (Myint, Gober, Brazel, Grossman-Clarke, & Weng, 2011). The object-oriented classifier segments the image through the merger of pixels into objects and the classification is conducted based on those objects instead of an individual pixel (Myint et al., 2011). Image segmentation is a principal function that splits an image into separated regions or objects depending on specific parameters (such as texture, continuity, spectral information, etc.) (DesclĂŠe, Bogaert, & Defourny, 2006; Im, Jensen, & Tullis, 2008; Myuint, Giri, Wang, Zhu, & Gillette, 2008). A group of pixels having similar spectral and spatial properties is considered an object in the object-based classification prototype. This approach has been widely used on high resolution images, especially in urban studies (Myint et al., 2011) but has also been used on complex landscapes as mangroves and forests (DesclĂŠe et al., 2006; Duro, Franklin, & DubĂŠ, 2012; Xie, Roberts, & Johnson, 2008; Yan et al., 2006) with very good results because it overcomes the problem of salt-
29
and-pepper effects found in classification results from the traditional per pixel approach (Xie et al., 2008). In other words, the heterogeneity in complex landscapes results in high spectral variation within the same land-cover class. Hence, when per-pixel classifiers is used, it is possible to get noisy results, due each pixel is individually grouped into a certain category confused by high spatial frequency in the landscape. Nevertheless, per-field classifier has shown to be effective for improving classification accuracy and deal with the problem of environmental heterogeneity (Aplin & Atkinson, 2001; Lloyd, Berberoglu, Atkinson, & Curran, 2004). However, it is important to develop a comparative study of different classifiers to find the best classification result for a specific study (Pal & Mather, 2004 ; South et al., 2004 ; Lu & Weng, 2007). 2.2.4. Accuracy Assessment In thematic mapping from remotely sensed data, the term accuracy is used typically to express the degree of "correctness" of a map or classification. A thematic map derived with a classification may be considered accurate if it provides an unbiased representation of the land cover of the region it portrays (Foody, 2002). Therefore, classification accuracy refers to the degree to which the derived image classification agrees with reality or conforms to the "truth" (Smits, Dellepiane, & Schowengerdt, 1999). Previous research has indicated that post-classification processing is an important step in improving the quality of classifications (Lu & Weng, 2004 ; Stefanov, Ramsey, & Christensen, 2001). The evaluation of the classification results is an important process in the classification procedure. Different approaches may be employed ranging from a qualitative evaluation based on expert knowledge to a quantitative accuracy assessment based on sampling strategies (Foody, 2002 ; Lu & Weng, 2007). Cihlar et al. (1998) proposed six criteria to evaluate the performance of a classification method: accuracy, reproducibility, robustness, ability to fully use the information content of the data, uniform applicability, and objectiveness.
Nevertheless, in reality, no classification
30
algorithm can satisfy all these requirements nor be applicable to all studies due to different environmental settings and datasets used. Classification accuracy assessment is however, the most common approach for an evaluation of classification performance (Lu & Weng, 2007). Congalton (1994) identifies four major historical stages in accuracy assessment. First, the accuracy assessment based on a basic visual appraisal of the derived map. A second stage characterized by an attempt to quantify accuracy more objectively based on comparisons of the areal extent of the classes in the derived thematic map (e.g. km2 or % cover of the region mapped) relative to their extent in some ground or other reference data set. The third stage involved the derivation of accuracy metrics that were based on a comparison of the class labels in the thematic map and ground data for a set of specific locations. These site-specific approaches include measures such as the percentage of cases correctly allocated. The fourth stage is a refinement of the third in which greater use of the information on the correspondence of the predicted thematic map labels those observed on the ground. This stage has the confusion or error matrix as a tool, which describes the pattern of class allocation made relative to the reference data. An important characteristic of this stage is that measures of accuracy that use the information content of the confusion matrix more fully than the basic percentage of correctly allocated cases, such as the kappa coefficient of agreement, are frequently derived to express classification accuracy. The kappa (K) coefficient measures the agreement between classification and ground truth pixels. A Kappa value of 1 represents perfect agreement while a value of 0 represents no agreement (ENVI EX, 2009).
Where:
31
ᵢ = the class number N = the total number of classified pixels that are being compared to ground truth mᵢ,ᵢ = the number of pixels belonging to the ground truth class ᵢ, that have also been classified with a class ᵢ (e.g. values found along the diagonal of the confusion matrix) Cᵢ = the total number of classified pixels belonging to class ᵢ Gᵢ = the total number fo ground truth pixels belonging to class ᵢ
As for Kappa coefficient interpretation, there is not a standardized way to do it. Several authors have their own considerations but for this research, the Fleiss (1971) scale of interpretation was used: Kappa Values
Consistency Strenght
< 0,20
Poor
0,21 - 0,40
Weak
0,41 - 0,60
Moderate
0,60 - 0,80
Good
0,81 - 1,00
Very good
32
3.
Materials & Methods 3.1.
Study Area
The invasive species in the Galapagos Islands are concentrated on 5 islands: Santa Cruz, Isabela, San Cristobal, Floreana and Santiago. These 5 islands present adequate conditions for the invasive species to growth, as they have high elevations and humid zones, were the problem of invasive species has become apparent with greater magnitude (Ministerio del Ambiente, 2013). Also, 4 of them (Santa Cruz, Isabela, San Cristobal and Floreana) are inhabited by residents, whom develop agricultural activities and importation of supplies.
Consequently, the study area has concentrate in the
highlands of San Cristobal island, which have evident problems specially with guava, waxapple and blackberry, and also is the island with better logistic conditions for the field work. The study area is located in the humid zone of the San Cristobal Island in the Galapagos Archipelago. It is made up of approximately 5,992 hectares from which the agricultural area covers 55% and the remaining 45% is part of the national park area (Map 1). The agricultural area is subdivided into private farms owned by the San Cristobal´s residents, while the national park area is a restricted to use area of conservation. The extent of the study area was limited based on the availability of images to date almost without cloud cover.
33
Map 1. Location of Study Area
34
3.2.
Characterization of the Area
The Galapagos Islands stretch over a 320 km axis from east to west and the equator line passes through the archipelago. The archipelago is made up of 19 islands and 42 islets. San Cristobal, the easternmost island of the Galapagos group lies about 1100 km west of the South American mainland. The total land surface of the archipelago is over 8000 km2. San Cristobal is the fourth bigger island with 558 km after Isabela, Santa Cruz and Santiago (Constant, 2000). The origin of the Archipelago is volcanic. It is a “hot spot” which is understood as a weakness of the oceanic crust. It is an area of high thermic flux and intense seismic and volcanic activity. Despite colonization of the islands has driven change in the ecosystem, these are oceanic islands which means that they have never been part of the continent and have their own ecosystems evolved through natural processes. Even though the islands are located on the equator, they do not have a characteristic tropical climate. The influence of the ocean and wind dynamics is an important factor in the islands climate. There are 2 marked seasons, each of which has a dramatic effect on the vegetation: 1) dry or garua season and 2) hot or wet season (Jackson, 2007). The dry or garua season lasts from June to December where the air is cooler, the skies are often lightly overcast and temperatures are lower than during the rest of the year. The Temperature of the water is about 18° to 20° C and the sea is often choppy.
A
frequent mist covers the top of the islands with some fine rain which is less frequent along the coast. The hot or wet season lasts from December to June where the temperature rises and the number of sunny days increases. The rain occurs during the first three months of the year and the water temperature ranges from 24° to 27° C and the sea becomes more calm.
35
The variation in rainfall with the altitude has important consequences for the zonation of the vegetation, and seven vegetation zones can be distinguished accordingly in San Cristobal: 1. 2. 3. 4. 5. 6. 7.
Litoral Zone Arid Zone Transition Zone Scalesia Zone Brown Zone Miconia Zone Pampa
Agricultural zone: The arrival of farmers on the island and the consequent farming activities have created ecological disturbance in the agricultural area which have formed transition, scalesia, Brown and Miconia zones. Agricultural activities have been the main reason for the introduction of foreign species, some of which are taking over the native species and have become invasive (Jackson, 2007). The increase in human population can be directly related with the introduction of alien species either intentionally or accidentally (Gonzรกlez et al., 2008). A very representative example is that of Guava which is one of the 10 most important introduced species. Many attempts have been made to eradicate it but have failed and the problem remains (Jackson, 2007).
3.3.
Data Sources
3.3.1. Ground Data The samples for land cover and land use types were collected in a field campaign, which took place on July 2011. Only 60 field sites could be recorded to inspect their land use / cover classes as a result of accessibility and budget restrictions.
A stratified random
sampling was made based on a first generated classification map prepared for the field campaign, the 60 points were collected in the field with restrictions mainly by the accessibility of roads.
Afterwards 27 validation points were selected on the high
36
resolution World View image, which were visually interpreted, and assigned to one of the land use/cover classes already defined. As, when using high resolution information, the analyst interpretation is not only accurate as collecting ground truth data, but also faster and cost-effective (Rozenstein & Karnieli, 2011). Nevertheless, not an even distribution for the classes was achieved. The 87 points from different types of LULC (land use and land cover) were registered in a projection Universal Transverse Mercator (UTM) 15 South projection. In Table 1 the number of field points within each class is detailed, and in map 2 the distribution of the field points on the study area are shown. Table 1. Ground data collected in the field Classes
Field Points
Abandoned Lands
5
Wax Apple Fields
11
Coffee Cultivation
9
Crops
12
Guava
13
Infrastructure
4
Bare Soils
0
Pasture
28
Natural Vegetation
5
Total
87
37
Map 2. Distribution of the field points within the study area
38
In addition, ground control points were registered by recording points such as road intersections and properties boundaries which can be identified in the image to geometrically correct the remote sensing image. The ground control data base accounts for 10 features, which was collected using a Trimble GeoExplorer 3000 XT, which handle a submeter accuracy using postprocessed differential correction. Table 2. Ground control data technical information Datafile R070715A.c or R070716A.c or R070716B.co r R070716C.co r R070717A.c or R070720B.co r R070815A.c or R070816A.c or R070817A.c or R070817B.co r
Max_PDO P
Std_Dev
GPS_Heigh
Horz_Pre
Vert_Pre
t
c
c
4,7
1,687213
208,407
1,331
3,155
5,1
0,602393
313,506
1,088
2,044
3,0
0,803196
394,238
1,075
2,163
3,7
0,718164
435,124
1,208
2,292
4,3
0,680232
344,004
1,238
2,211
5,1
0,702609
550,116
1,110
2,344
5,9
1,331810
310,000
1,307
2,574
6,0
3,020518
210,295
1,871
4,535
5,6
2,922298
213,959
1,501
3,690
5,8
1,753699
329,013
1,591
4,445
*The PDOP value indicates the quality of a GPS position. It takes account of the location of each satellite relative to the other satellites in the constellation, and their geometry in relation to the GPS receiver. (Trimble, 2016)
39
26 ground control points are scattered in the area inhabited of the San Cristobal Island. From which 10 were recorded within the study area, as is shown in the map 3.
40
Map 3. Ground control data
41
3.3.2. Secondary Data The secondary information available was a high resolution World View II image collected on October 23, 2010, with 6.4% of cloud cover. This image covers approximately 40% of the western part of the study area. In addition, a geodatabase with basic datasets such as the island boundaries, roads, properties boundaries, agricultural boundaries among others was obtained from the municipality of Puerto Baquerizo Moreno.
All the
geographic information used in this study is World Geodesic System (WGS84) map projection, 15 south. 3.3.3. Satellite Data A Landsat ETM+ image of the study area was obtained on March 21, 2011 (WRS Path 17 â&#x20AC;&#x201C; Row 61) from which 6 multi-spectral bands were used (1,2,3,4,5,7) with a resolution of 30 x 30 m. The cloud coverage corresponds to 6,57 % of the scene and affects 12,2% of the study area. This area is usually cover by dense clouds, in consequence this image was selected considering the year and it´s low cloud coverage on the study area. Also, this data is typically affected by a breakdown of one of the sensors. However, the coverage of the study area is completely unaffected by such damages. The image was cut to fit the study area and then it was masked in order to eliminate the small amount of clouds. Finally, a geometric rectification of the imagery was undertaken using a first order polynomial with a nearest neighbor interpolation, incorporating the DEM with a 10 ground control points, producing a RMSE of less 0.5 pixels (7.5m).
3.4.
Methods
Methods were divided into pre-processing, classification and validation components, in brackets (see the workflow in Figure 3). Pre-processing included geometric rectification of the Landsat image and all other secondary data. Assessment of classification methods
42
was made using accuracy of 9 land classes using a Kappa index.
The methods were
adjusted using a â&#x20AC;&#x153;trainingâ&#x20AC;? data base obtained through field observations and a high resolution image from World View II. The image pre-processing and classifications were made using the ERDAS 2011 and ENVI 5 softwares.
Figure 3. Workflow
43
3.4.1. Hybrid Supervised - Unsupervised Classification To obtain classes with optimal spectral separation, this approach combines an unsupervised classification algorithm, spectral signatures depuration and supervised classification algorithms (Messina et al., 2000). The values presented in table 3 were selected through processing experience and literature reports (Messina & Walsh, 2001 ; Ministerio de Medio Ambiente, 2010).
Table 3. Hybrid Supervised â&#x20AC;&#x201C; Unsupervised Classification steps Unsupervised Classification
Signature Evaluation
Supervised Classification
ISODATA 255 Classes
Evaluate Separability Transformed divergence
Input the edited signature set Parametric & Non-parametric
24 Itinerations 0,98 Convergence
> 1950 for acceptability
methods described in figure 3
Fuente (Ministerio de Medio Ambiente, 2010)
During the unsupervised classification, 255 classes were established based on the fact that 255 is the most number of classes that can be selected while being able to maintain an 8 bit data structure (Messina & Walsh, 2001). This allows a separation of the major land cover categories with a minimum of spectral information overlap (Mena & Ozdenerol, 2001). A preliminary attribute for each class was identified with visual analysis of the obtained categories through the overlap of the classification using the World View II image. After the first appreciation of the classified area, the separability of the spectral signatures was analyzed using the Transformed Divergence method which allowed the reduction of the original 255 spectral signatures into 36 spectral signatures or classes with a signature separability threshold of >1950, generating very limited overlap between classes (Messina & Walsh, 2001). These 36 categories were assigned to 9 classes through visual analysis which represent the most meaningful units of the study
44
area: Crops, Abandoned Lands, Coffee Cultivation, Wax Apple Fields, Guava, Pastures, Bare Soils, Natural Vegetation, and Infrastructure. Once the statistical separable classes were identified, a supervised classification was carried out testing the different parametric methods available in the ERDAS 2011 software (Mahalanobis Distance, Minimum Distance and Maximum Likelihood). After all methods were processed in the supervised classifications, a visual evaluation was performed though the overlap of the image with the training data base and with secondary information from various sources. The aim of this step was to verify first-hand the consistency of the obtained information. An accuracy assessment of the data was carried out through a confusion matrix from which a Kappa index was determined.
3.4.2. Principal Components with Hybrid Supervised - Unsupervised Classification This approach manages the same proceedings for the above mentioned method â&#x20AC;&#x153;Hybrid Supervised - Unsupervised Classificationâ&#x20AC;?. However, the Principal Components from the original image were used through a spectral data analysis of the imageâ&#x20AC;&#x2122;s Principal components. Principal components analysis (PCA) are a spectral enhancement technique that compress the spectral bands and extract new bands of data eliminating the noise and the redundant information through statistical algorithms (Ceballos & Bottino, 1997; Erdas Inc, 1999). For doing so, the ERDAS principal component analyst was used to obtained 6 PC bands (which are the same number of bands that the original landsat image had). It is important to notice that the first principal component stores the maximum contents of the variance of the original data set and the second PC describes the large amount of the variance in the data that has not been described by the first PC and so on (P. Taylor, 1977). Then, the eigenvalue of each band was analyzed and the number of the PC bands that were chosen to enter into the classification analysis was determined.
45
3.4.3. Object Based Hybrid Supervised - Unsupervised Classification The segmentation was developed according to the process described in figure 4.
Figure 4. Feature extraction process. (Source: ENVI EX, Feature Extraction Tutorial)
The parameters used for the segmentation process were established for the specific area as presented in table 4. These values were established based on a trial and error test within the different segment, merge, and refine settings, trying to detect which of the different configuration in the Feature Extraction segmentation interface of the ENVI software suited better the grouping objects in the study area. For doing so several spatial (area, compactness, etc.) and spectral (standard deviation of each band) parameters were considered. Also, a visual verification of the objects was part of the process. The basic premise was that the objects should be as homogenous as possible. A final shapefile with all the information of the attributes was obtained from this process. Table 4. Parameter Settings for the Feature Extraction
46
Based on the image result of the segmentation, the same procedure for the above mentioned method “Hybrid Supervised - Unsupervised Classification” were performed.
3.4.4. Validation Method To evaluate data precision, a confusion matrix was produced using verification points established with the sample size which was determined using the following formula (Magnani, 1999): Ss ¿
Z ²∗( p )∗(1−p) c²
Where: Z = Z value à 1,96 for a 95% confidence level. p = percentage à 0,5 c = confidence interval à 0,01
new ss=
ss ss −1 1+ pop
Where: Pop = Population à 66576 pixels
47
The sample number accounted for was 96 sample points from which only 87 points were used due to lack of availability of primary information. The assessments of the classification accuracy of the LULC maps were conducted by comparing ground true data and the classified layers (Congalton, 1991). For ease of comparison between classification methods, thematic accuracy was undertaken using only the point-based reference data mention above. There was no consideration of any object-based accuracy assessment or accuracy measures relating to the geometric accuracy of the objects (such as location and shape) (Whiteside, Boggs, & Maier, 2011 ; Aguirre-GutiĂŠrrez, Seijmonsbergen, & Duivenvoorden, 2012). Accuracy assessments of all 9 classification were undertaken using confusion matrices and kappa statistics. Producer and User accuracies for each class were calculated as well as the overall kappa coefficient. The statistical significance of the difference between the kappa coefficients for the pixel based, principal components and object based classifications was assessed using a marginal homogeneity test. The confusion matrix evaluates the consistency of the samples with a Kappa index which should be interpreted according to the following scale:
Kappa Values
Consistency Strenght
< 0,20
Poor
0,21 - 0,40
Weak
0,41 - 0,60
Moderate
0,60 - 0,80
Good
0,81 - 1,00
Very good Source: (Fleiss, 1971)
48
4. Results Annex 1 shows classification results for all the methods used. Pixel-based supervised/unsupervised classification, supervised with principal components and object oriented classification. All these methods are using a combination of pixel selection rules: maximum likelihood, Mahalanobis distance, and minimum distance. 4.1.
Principal Component Selection
It was determined that only the first 2 strips of the Main Components carry most of the information of the original image given that this is where 98,81% of the imageâ&#x20AC;&#x2122;s information is concentrated as shown in Table 5. Table 5. Spectral Analysis of the Principal Components Basic
Mi
Ma
stats
n
x
Band 1
0
221
Band 2
0
196
Band 3
0
211
Band 4
0
153
Band 5
0
196
Band 6
0
162
Mean
Stdev
57,97
28,18
7 47,67
0 23,51
6 35,05
0 17,78
8 78,48
6 39,45
2 69,22
5 35,02
0 31,56
2 16,60
2
3
4.2.
Nu
Eigenvalu
m
e
1
4552,11
2
113,95
2,41
3
42,58
0,90
4
7,69
0,16
5
4,08
0,09
6
1,72
0,04
4722,13
100
% 96,4 0
Object Based - Segmentation Image
49
Within the object-based analysis, the segmentation provided 6702 objects for classification. It was visually apparent that objects of this size can still contain more than one spectrally distinct land cover. The mean size of the objects was of 8940 m² while the smallest object was of 1800 m² and the biggest one was of 44100 m². The object image is shown in figure 5
Figure 5. Image segmentation result - Object based image
50
4.3.
Classification Comparison
The comparison of the area (ha) assigned to each class is presented in table 6.
Table 6. Area (ha) of each class for the three approaches
According to the three classification approaches, the land cover class occupying the largest area is "Guava" (PB: 1801.14 ha.; PC: 1716.48 ha.; OB: 1837.92 ha), while the smallest area is "infrastructure" (PB: 21 ha.; PC: 17.19 ha.; OB: 18.96 ha). Also, this table allowed us to identify certain differences between the area of some classes. Just to mention, the area classified as "pasture" is noticeably lower in the pixel-based classification compared to the two other classification approaches. In the same way, "Natural Vegetation" shows a higher value in the pixel-based classification compared to the other approaches.
Nevertheless, the area of classes such as "Infrastructure",
"Guava", and "Wax Apple Fields" are relatively similar in the three approaches. Overall, there is a consistency in the area results of the three tested approaches, as is shown in figure 6.
51
(ha
2000
1500 PB_Mahalanobis Distance PB_Maximum Likelihood PB_Minimum Distance PC_Mahalanobis Distance PC_Maximum Likelihood PC_Minimum Distance OB_Minimum Distance OB_Maximum Likelihood OB_Mahalanobis Distance
1000
500
Wax Apple Fields
unclassified
Pastures
Natural Vegetation
Naked Soil
Infrastructure
Guava
Crops
Coffee
Abandoned Lands
0
Figure 6. Area values (ha.) for each class, within each classification method.
All the above mentioned findings were supported by a visual comparison with the classification images in map 4 where the classification results for maximum likelihood of each approach are shown (for more information consult annex 1 where the results for every parametric decision rule for each approach are presented). It is possible to visualize that the "Natural Vegetation" class is apparently under-represented in the object based classification and especially in the principal component pixel based classification while there is an apparent over-representation of the "Pasture" classes. On the other hand, in accordance to the area values, it is possible to identify a consistency in the identification of "Guava", Wax Apple Fields" and "Crops" given that the three results are visually very similar. While pixel based and object based methods produce aggregation of pixels based on land cover classes, the object based classification yields multi-pixel features whereas the pixel
52
based classification contains many small groups of pixels or individual pixels. This produces classes with mixed clusters of pixels as displayed by the heterogeneous nature of the Pixel based classified image. Nevertheless, in map 4 it is possible to see that pixel based classification presents the "salt and pepper" effect on a very low level (this is not the case of the principal component approach) and it´s representation of the territory composition is very good and detailed. The object based classification shows a more aggregate result that in some cases shows too much generalization on the class boundaries. In this sense, "Natural Vegetation" seems to cause confusion with the other classes as "Coffee", "Pastures" and "Crops", especially on the Principal Component approach, and the same is seen to happen with "Bare Soil" which seems to be overrepresented on the Principal Components and Object based approaches. All these variation on classes representation that is possible to read by a visual inspection is supported by the standard deviation graphic presented in figure 7. Which clearly shows that Natural Vegetation and Pastures are the ones with more variation in the area results, meanwhile infrastructure, crops, guava and wax apple have less variation on the 9 classification results.
STD 250.000 200.000 150.000 100.000 Area (ha)
STD
50.000 0.000
Figure 7. Standard deviation of the area (ha) results for the 3 classification approaches
53
Map 4. Classification results for Maximum Likelihood for each approach
54
4.4.
Accuracy Assessment
4.5.
An
accuracy
performed
for
assessment each
was
classification
produced in this study to evaluate how well
the
different
approaches
performed. A confusion matrix (cross tables) was built comparing the results with the reference data.
Table 7
contains the main results and figure 8 presents the kappa index calculated for each
approach.
The
complete
information for each classification is presented in annex 2. 4.6. 4.7.
Table 7. Accuracy assessment results for
the
pixel
based,
principal
component - pixel based and object based classifications approaches.
55
4.8.
4.9. 4.10. 4.11.
56
4.12.
0.9000 0.8500 0.8000 0.7500 0.7000 Kappa Index
0.6500 0.6000 0.5500 0.5000 0.4500 0.4000 Maximum Likelihood
Mahalanobis Distance
4.13. Figure 8. Kappa results 4.14. 4.15. Overall,
the pixel based classification
had a better outcome in all 3 parametric decision rules based on the kappa
index
(Maximum
Likelihood:
0.8640; Mahalanobis Distance:
0.8610;
Minimum Distance: 0.7959) which can be
57
interpreted as a very good consistency strength according to Fleiss (Fleiss, 1971).
While the object based
classification had an underperformance compared to the other two methods (Maximum
Likelihood:
0.7029;
Mahalanobis Distance: 0.5735; Minimum Distance: 0.5429), "Abandoned land",
"Infrastructure" and "Coffee" obtained the higher user´s and producer´s accuracy values (over 70% user´s and producer´s accuracy). It is worthy to mention that there may be classes with fewer ground data samples, such as "Natural Vegetation", "Bare Soil" and "Infrastructure" and this evidences a possible bias in the accuracy results. 4.16. Also,
a marginal homogeneity test was
used to identify if the similarity between the classification results and the reference data was statistically significant.
The
pixel
based
classification was the approach with better outcomes as expected. Both, Maximum Likelihood and Mahalanobis Distances decision rules turned out to be statistically similar with p values of 0.2714 and 0.2714 respectively. The Pixel based - Principal Component
58
Mahalanobis Distance had a p = 0.1. The difference in overall classification results was not statistically significant (p<0.05), as shown in table 8. 4.17. 4.18. Table
8.
Marginal Homogeneity
significance values
4.19.
4.31.
4.43.
4.20.
4.21.
4.22.
4.24.
4.25.
4.26.
4.28.
4.29.
4.30.
4.32.
4.33.
4.34.
4.36.
4.37.
4.38.
4.40.
4.41.
4.42.
4.44.
4.45.
4.46.
4.48.
4.49.
4.50.
4.52.
4.53.
4.54.
4.55. 4.56. Specifically
in the case of the invasive
species Guava and Wax Apple, the accuracy results were over the 80% for the Pixel based approach. As for the object based and principal components the percentage of accuracy decrease up to 50%. Results information is shown in table 9. 4.57.
59
4.58. Table 9. User´s and Producer´s accuracy
for Guava and Wax Apple Fields
4.59. 4.60. For
visual
demonstration
of
the
classification results of Guava and Wax
60
apple, map 5 shows a subset of small areas of interest. As is possible to see Wax apple has a strong signature, and is very well defined in all the classifications methods. Nevertheless, pixel based classification is the one that shows a better delimitation detail of the wax apple fields area. The object based classification present a more coarse delimitation, where some of the border details are lost. On the other hand, the guava results varie, the principal
components
classification
shows a strong salt and pepper effect, and some confusion with the signature of pastures, bare soil and abandoned lands.
The pixel based and object
based approach give a more solid result. 4.61. It
is important to mention that these
invasive
species
characterize
for
dominate big areas, and allowed for a medium resolution image to capture its spectral characteristics. Been not the same for species like blackberry, which grows understory of other endemics or not endemics of the islands, making
61
very difficult to identify even with high resolution images.
62 4.62. 4.63. Map 5. Subset area for Invasive Species classification review for each of the 9 results.
63
4.64. 4.65. A
Discussion
Landsat TM image was pre-
processed and classified using three methods:
pixel based, principal
component - pixel based and object based.
For all three approaches
hybrid (supervised - unsupervised) classification was applied because it has
proven
accurate
to
produce
results
supervised
compared
or
classifications
to
unsupervised (Rozenstein
Karnieli, 2011).
very
&
Also, the three
parametric decision rules were applied method
for
each
classification
(Maximum
Mahalanobis
Likelihood,
Distance
Minimum Distance).
and
Hence, 9
classification results were evaluated to identify which approach and parametric rule perform was the best outcome of the representation of the study area landscape. 4.66. In
general,
the
classifications
produced using either pixel based, principal components - pixel based or object based image classification, created
similar
and
visually
acceptable representations of the land cover classes within the study
64
area. As expected, the pixel based classifications compared to the object based classification, offered a more generalized visual appearance and more contiguous interpretation of land cover. Despite that the "salt and pepper" effect in the pixel based analysis was expected, it was not evident even though the pixel based images did not receive any additional treatment. This was not the
same
with
Component
the
Principal
approach,
which
presented a lot of variation in the classification
result
for
small
clusters of pixels and even for individual pixels. 4.67. Answering
the
first
research
question of which classification approach
represents
ecosystems
complexity
the at
the
Galapagos Islands in the most accurate
way,
the
pixel-based
classification had a better outcome from evaluation than the other approaches given that the overall accuracy of this approach had a higher kappa in the three tested parametric decision rules.
The
Object Based classifications had the lower kappa index for the same
65
three tested parametric decision rules.
Also,
the
marginal
homogeneity test performed a comparison
between
classification
each
outcome
referential data.
and
the
Despite the low
sample size of the test set and associated wider confidence limits, revealed that the obtained results strengthen that the Pixel Based approach
using
Maximum
Likelihood or Mahalanobis Distance decisions
rules
have
a
better
the
other
performance
than
approaches.
As for the second
research
question
classification sensitive
of
approach towards
which is
most
identifying
invasive species such as Guava and Wax Apple fields at the Galapagos Islands. The accuracy results for both species shown to be higher with the pixel based approach, specifically for maximum likelihood and mahalanobis distance. 4.68. As mentioned,
the study area of the
agricultural zone of San Cristobal Island
is
very
complex,
due
countless social processes have been taken place and continue reshaping
the
territory.
As
66
examples, the abandonment of the agricultural farms, the rapid spread of the invasive species as Guava, Blackberry and Wax Apple and the very sensitive but in some cases not managed boundary between the National Park and the agricultural area, have created a very diverse landscape with a high mixture of plant composition in very small pieces of land. In this sense, when we use the object based approach it is evident that a mean size object of almost one hectare, in such a complex landscape, may contain more than one spectrally distinct land cover given that it tends to generalize the results. This can be visualized in the classification image results (annex 1).
This particular
characteristic of the object based classifier have been recognized as a design tool to deal with the problem
of
heterogeneity
environmental and
indeed
has
shown to be effective in some cases for improving classification accuracy (Aplin & Atkinson, 2001; Lloyd et al., 2004). In this sense, the object based
approach
may
conceive
classes as a redefined concept
67
which transforms the traditional land cover or vegetation classes into more contextual classes (Whiteside et al., 2011) but might sometimes be difficult to apply in complex systems. Also, this type of approach has shown to work better on high resolution images and in more homogenous landscapes such as the urban areas where it has proved very good outcomes (Whiteside et al., 2011). Therefore, when dealing with very diverse landscapes such as the study area and by using a medium spatial resolution image, the segmentation outcome may be mixing
spectral
responses
of
different elements in one object and
lose
information
in
the
classification image. 4.69. On
the other hand, when the
results
for
both
pixel
based
approaches are analyzed, it is evident even by simple visualization that
the
principal
classifications
tend
component to
mix
signatures responses. So, contrary to what Ceballos and Bottino found (Ceballos & Bottino, 1997),
the
principal components image that was created from the first two PCs
68
which
contain
98,81%
of
the
information, did not show the same discrimination ability than the sixwaveband original information did. It may be the case in which the discarded PCs may prevail necessary for proper discrimination (Li & Yeh, 1998).
As a consequence, an
overestimation of some classes and underestimation of others appears to have happened and a visible "salt and pepper" effect can be evidently seen.
Meanwhile, the pixel based
approach
captured
the
heterogeneity of the agricultural landscape
with
a
very
good
consistency and it showed a better reading of the spectral signal of the classes compared to the other two approaches. 4.70. The
hybrid classification method
that was used was considered the most suitable for the classification tasks. As an example, even though the supervised classification has showed
to
work
better
than
unsupervised classification, when treating heterogeneous landscapes, it seems to behave in the opposite way given that training does not account for all the complex spectral
69
variations of land-cover in the area. Consequently,
one
must
be
intimately familiarized with the research area to be able to train the classifier in a proper way. using
a
hybrid
Thus, method
(unsupervised
-
supervised
classification)
is
convenient
especially with large data sets and heterogeneous landscapes (Erdas Inc, 1999). In addition, this mixture of techniques allows to capture different mixed spatial and spectral classification
schemes
which
preserve natural variability of the landscape for pre-set LULC classes to be characterized (Messina et al., 2000) and in this way it improves the classification accuracy with respect
to
standard
spectral
classification
methods.
Also,
derived from the three parametric decision rules tested, Maximum Likelihood is the one that had better results within the three approaches (Object Based, Pixel Based, Pixel Bases - Principal Component), as it is presented in figure 9.
70
4.71.
4.72. Figure
9.
kappa
results
for
each
classification method 4.73. The
accuracy of the classification
was tested using cross tables to calculate the kappa index and by calculating
the
homogeneity test.
marginal Besides the
results that have been explained, it is important to mention that there could be a bias in the accuracy results given that the sample size (87) is small and not even for each class. This could be affecting the
71
overall analysis. Also, it is essential to consider that the decision of caring out a sampling effort that is economically
feasible
and
logistically possible, with one that allows
for
statistically
rigorous
comparisons
is
a
major
consideration
in
operational
settings where resources are often limited (Congalton, 1991). 4.74. Remote
Sensing
and
Invasive
Species 4.75. This
methodological exercise was
performed to monitor land cover using remotely sensed data which requires
robust
classification
methods to allow accurate mapping of complex land cover and land use categories
(Rodriguez-Galiano,
Ghimire, Rogan, Chica-Olmo, & Rigol-Sanchez,
2012).
In
this
context, the use of remote sensing techniques and the evaluation of different mapping techniques for the identification of invasive plant species in the Galapagos island is a major need.
In this sense, tools
that not necessarily require physical contact in the field are of major importance
(Schott,
1997)
72
especially in the case of the Galapagos
islands
which
have
restriction on accessibility to most of the areas of the National Park. 4.76. In
a general context, invasion by
alien plant species is among the greatest threats to ecosystems and human wellbeing (Joshi, Leeuw, & Duren, 2004). As such, biological invasions have been identified as a major non-climatic driver of global change (Vitousek & Walker, 1989) and recognized as a primary cause of global biodiversity loss (Czech & Krausman, 1997; Wilcove & Chen, 1998), as well as influencing the metabolism of ecosystems and altering
disturbance
regimes
(Vitousek, 1990). Land-use change and other human activities are important factors involved in the process of invasion, establishment and
expansion
(Hobbs,
2000;
Vitousek et al., 1997). 4.77. Remote
sensing technology has
attracted considerable interest in the field of invasive species in recent years. It is a tool that offers proven advantages that include a synoptic view, multispectral data,
73
multitemporal coverage and cost effectiveness
(Van
der
Meer,
Scmidt, Bakker, & Bokler, 2002). It has also proven to bring a practical approach when studying complex geographic
terrain
types
and
diverse inaccessible ecosystems like the ones present in the Galapagos islands. 4.78. Guava,
blackberry,
wax
apple,
among others are several of the invasive species that threaten the sensitive
ecosystem
Galapagos Islands.
of
the
Very costly
efforts have been made to eradicate the fore mentioned species but none have had the desired effect. Currently, management of invasive species aims at controlling invaders and mitigating their impact rather than
aiming
at
eradication.
Limitation of resources forces land managers to carefully plan and prioritize interventions only in areas that are most severely affected by invaders. For this important reason, information on the current and potential distribution of invaders is considered
crucial
for
their
management (Joshi, 2006). For doing so, it is important to consider
74
that these methods should be applied to broad areas and for budget and accuracy interests, they should be based on free images (when possible) and on specific methodological procedures which could
be
easily
replicated
by
professionals in different areas. 4.79. Several
pros and cons emerge from
the statement mentioned above. According to Chudamani (Joshi et al., 2004), invasive species can be categorized into 4 classes: Class I includes species dominating the canopy and forming homogeneous single species stands.
Class II
includes species that are members of a multi species canopy and directly reflects electro-magnetic radiation. Class III includes species not reflecting but influencing the reflective
properties of
canopy
members belonging to class I and II. Class IV includes all species that neither reflect light nor influence the reflective properties of others species in class I and II.
It was
noticed that most studies have focused on the class I species because it is harder to develop techniques for the other classes. In
75
this sense, several factors related to the research question should be taken into consideration as well as the characteristics of the invasive plant species should be understood. 4.80. In
the specific case of this study, a
multispectral image with a medium spatial resolution of 30m, which makes it possible to detect large stands
and
patches
through
classification, which can be enough to identify guava or wax apple but may not be the same for blackberry. This methodology can be very useful to deal with the invasion of Guava which is considered one of the most aggressive and harmful invader in the island of San Cristobal.
In
addition,
other
approaches should be developed to deal with other invaders that have not been identified and future investigation should focus on using different
methods
such
as
monitoring vegetation dynamics by using vegetation index time-series data
derived
from
inexpensive
images (Huang & Asner, 2009) as well as other remote sensing techniques that focus on species with understory characteristics like
76
the blackberry (Joshi et al., 2004); (Huang & Asner, 2009).
77
4.81.
Conclusion
4.82. Management
dealing with invasive
species requires accurate mapping and modeling techniques at relative low costs. Development of those will be valuable step towards conservation and native biodiversity (Joshi et al., 2004). This statement fulfill current situation in Galapagos Islands, where the invasive species problem is radicated in populated and non-populated islands, and efforts by non-profit organizations and public and private institutions have
been
disbanded
and
territorially unplanned, giving no effective solution for such problem. Having a methodology that could be easily replicated and low cost founding can make the difference between real intervention impacts and locally efforts in this war we must wage. 4.83. To
identify a simple but effective
remote sensing method could fulfill current needs and become the sources of generation of valid and updated information, which can feed
decision-making
process.
However, to do this it is important
78
to consider the natural conditions of
the
territory
and
try
to
determinate the method that adjust better and produce better result using available data source such as Landsat Images.
In this context,
several things can be mention: 4.84. The
three tested methods are
simple methods of classification and have been tested in different type of
landscapes
showing
to
be
effective classification techniques. Nevertheless, for the study area in the San Cristobal agricultural area in the
highlands,
the
per
pixel
classification have shown to be more effective than the other two techniques. These results respond to the complexity of the landscape, which presents a very diverse ecosystem, with a high mixture of plant composition in very small pieces of land. In this sense, pixel based method respond in a better way to the diversity of spectral signature, while the object based approach
generalize
the
classification outcome, because it is based on a mean size object of almost one hectare, and in such a complex landscape, may contain
79
more than one spectrally distinct land cover. 4.85. The
results for both pixel-based
approaches are superior than the object
based
approach,
nevertheless it was evident that the principal component classifications tend to mix signatures responses, and did not show the same discrimination ability than the sixwaveband original information did. It may be as the discarded PCs might prevail necessary for proper discrimination,
and
an
overestimation of some classes and underestimation
of
others
are
notice. 4.86. Large
swath width and pixel size are
unique
characteristics
of
high
temporal resolution images and can frequently monitor the spread of alien plants over a broad region. However, images would capture not only
the
species
but
other
components such as untargeted plants, surface soils and senescent vegetation which would limit the ability of images for invasive plant monitoring
unless
one
species
dominates an entire system (Huang
80
&
Asner,
2009).
As
it
was
mentioned, most of the remote sensing
for
invasive
species
approaches have been oriented to species that dominate de canopy or are members of a multi species canopy and directly reflects electromagnetic radiation. other
approaches
Nevertheless, should
be
developed to deal with other invaders
with
different
characteristics, which have strong effect in the natural ecosystems as is the case of blackberry in the Galapagos. In these sense, future investigation should focus on using different methods, which may allow to identify the presence of these type
of
ecosystems. 4.87.
invasive
species
in
81
4.88.
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Annex
4.183.
4.184.
Annex
results
1.
Classification
for
Maximum
Likelihood, Distance
Mahalanobis and
Minimum
Likelihood for each approach (Pixel
Based,
Principal
Component - Pixel Based, Object Based Classifications)
90 4.185. 4.186.
91 4.187. 4.188. Mapa 6. Classification results for Maximum Likelihood, Mahalanobis Distance and Minimum Likelihood for each approach (Pixel Based, Principal Component - Pixel Based, Object Based Classifications)
92
4.189.
Annex 2. Confusion matrix for each
classification
93 4.190. 4.191.
4.192.