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: Karl Atzmanstorfer
Quito - Ecuador, October 24th 2016
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
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.
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.
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.
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
3.4. 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
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
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
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
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 2013- 2017, 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).
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.
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
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
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.
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 &
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 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.
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
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,
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
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
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.
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
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¯¹ = 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.
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 multi- spectral 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).
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 per- pixel 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.) (Desclée, 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 (Desclée et al., 2006; Duro, Franklin, & Dubé, 2012; Xie, Roberts, & Johnson, 2008; Yan et al., 2006) with very good results because it overcomes the problem of salt-
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 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:
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
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, wax-apple 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.
Map 1. Location of Study Area
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.
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. Litoral Zone 2. Arid Zone 3. Transition Zone 4. Scalesia Zone 5. Brown Zone 6. Miconia Zone 7. 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 (González 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 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
Map 2. Distribution of the field points within the study area
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 Max_PDOP Std_Dev GPS_Height Horz_Prec Vert_Prec
R070715A.cor 4,7 1,687213 208,407 1,331 3,155
R070716A.cor 5,1 0,602393 313,506 1,088 2,044
R070716B.cor 3,0 0,803196 394,238 1,075 2,163
R070716C.cor 3,7 0,718164 435,124 1,208 2,292
R070717A.cor 4,3 0,680232 344,004 1,238 2,211
R070720B.cor 5,1 0,702609 550,116 1,110 2,344
R070815A.cor 5,9 1,331810 310,000 1,307 2,574
R070816A.cor 6,0 3,020518 210,295 1,871 4,535
R070817A.cor 5,6 2,922298 213,959 1,501 3,690
R070817B.cor 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)
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.
Map 3. Ground control data
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 – 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
was made using accuracy of 9 land classes using a Kappa index. The methods were adjusted using a “training” 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
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 – Unsupervised Classification steps
Unsupervised Classification Signature Evaluation Supervised Classification
ISODATA Evaluate Separability Input the edited signature set
255 Classes Transformed divergence Parametric & Non-parametric methods described in figure 3 24 Itinerations > 1950 for acceptability
0,98 Convergence
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 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 “Hybrid Supervised - Unsupervised Classification”. However, the Principal Components from the original image were used through a spectral data analysis of the image’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.
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
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=Z²∗(p) ∗(1−p) c² Where:
Z = Z value 1,96 for a 95% confidence level.
p = percentage 0,5
c = confidence interval 0,01
new ss = ss 1 +ss − 1
pop
Where:
Pop = Population 66576 pixels
Fase Parameters Settings Observations
Segmente Algorithm Intensity
Segment value 10
Merge Algorithm Full Lambda Schedule
Merge value 0
Texture Kernel Size 3
Compute
Attributes Including the 6 image´s strips
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-Gutiérrez, 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)
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 image’s information is concentrated as shown in Table 5.
Table 5. Spectral Analysis of the Principal Components
Basic
stats Min Max Mean Stdev Num Eigenvalue %
Band 1 0 221 57,977 28,180 1 4552,11 96,40
Band 2 0 196 47,676 23,510 2 113,95 2,41
Band 3 0 211 35,058 17,786 3 42,58 0,90
Band 4 0 153 78,482 39,455 4 7,69 0,16
Band 5 0 196 69,220 35,022 5 4,08 0,09
Band 6 0 162 31,562 16,603 6 1,72 0,04
4722,13 100
4.2.
Object Based - Segmentation Image
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
