Remote sensing and image interpretation have become standard approaches for mapping land use and land cover (LULC). Remotely sensed imagery can not only cover large spatial extents but can also capture information on features of small grains and extents, particularly because of the increased availability of high spatial resolution data products. Image interpretation and GIScience methodologies include automated approaches for mapping that are efficient and easily repeatable, which can reduce the costs associated with
in situ data collection and field visits. Further, remote sensing can provide information on
areas that are difficult to access because of isolation, difficult terrain, or other constraints (e.g., private property or protected sites).
There are many methodological approaches for characterizing LULC with remotely sensed data, each with their own advantages and disadvantages. Generally, the automated approaches can be subdivided into two general categories based on whether the clustering of pixels into classes is based on training information provided by the user (supervised) or is derived from the data itself (unsupervised). Of the many supervised classification
approaches that exist, two have emerged in recent years for characterizing LULC from high spatial resolution imagery: Support Vector Machine (SVM) and Object Based Image Analysis (OBIA). In this chapter, SVM and OBIA classifiers are evaluated for mapping LULC in southern Isabela Island, Galápagos with a WorldView-2 satellite image.
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SVM is a non-parametric machine learning algorithm. SVM classifications use training data samples to determine the optimal linear boundary between pairs of classes (Vapnik, 2000). Image data, often pixels, are then assigned to discrete classes based on their position in feature space with respect to the decision boundary. Studies employing SVMs for remote sensing applications have significantly increased in recent years, although the method has not yet been widely adopted (as reviewed by Mountrakis et al., 2011). SVM applications range from vegetation mapping (Boyd et al., 2006; Huang et al., 2008; Lardeux et al., 2009; Dalponte et al., 2008) and crop identification (Wilson et al., 2004; Karimi et al., 2006; Mathur and Foody, 2008), to urban feature extraction (Zhu and Blumberg, 2002; Inglada, 2007; Huang and Zhang, 2009), and LULC mapping (Huang et al., 2002; Keuchel et al., 2003; Pal and Mather, 2005; Dixon and Candade, 2008; Watanachaturaporn et al., 2008; Warner and Nerry, 2009; Li et al., 2010). One advantage of SVM classifiers is that they tend to produce higher classification accuracies than statistical classification approaches because they are less sensitive to the size of training data sets or to the manner of sample collection (Mantero et al., 2005).
OBIA is a rule-based classification approach that integrates image processing and GIS functionalities in the classification of non-overlapping image segments that represent features of interest. Image data are first partitioned into homogenous groups of pixels, image objects, that comprise real world objects (e.g., buildings, trees, or fields). Knowledge-based membership functions that explicitly define the rules for classification are then applied at the object level rather than on a per-pixel basis. In addition to the spectral data contained in the image bands, contextual information, such as the spatial, topological, and textural
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parameters for OBIA classification (Lang, 2008); the inclusion of contextual information often improves classification accuracy (Benz et al., 2004). Object-based classifications of LULC have become increasingly popular (Hay et al., 2005) in step with the availability of high spatial resolution imagery and commercial OBIA software (Blaschke et al., 2000; Blaschke and Hay, 2001). As recently reviewed by Blaschke (2010), OBIA has been applied to a wide variety of applications including the delineation of forest cover types (Dorren et al., 2003; Heyman et al., 2003), habitat mapping (Weiers et al., 2004; Bock et al., 2005, Lathrop et al., 2006, Diaz Varela et al., 2008; Jobin et al., 2008), general LULC assessments (Rahman and Saha, 2008; Platt and Rapoza, 2008), and in numerous studies of urban land use/cover (Kong et al., 2006; Chen et al., 2007; Stow et al., 2007). An advantage of OBIA is that objects are the basic unit of analysis and thus avoid the ‘salt-and-pepper’ effect in pixel- based classifications derived from high resolution data (Blaschke et al., 2000).
2.1.1 Study Aims
In this chapter SVM and OBIA classification approaches are evaluated for mapping LULC with high spatial resolution imagery. Two research questions are specifically
addressed:
1) How do the LULC classification results from SVM and OBIA differ?
2) Which classification approach is more effective in distinguishing and mapping LULC as measured by the accuracy of the resulting thematic maps?
To this end, pixel-based (SVM) and object-based (OBIA) classifiers are applied to a recent image (acquired 23 October 2010) of the southeastern slope of Sierra Negra Volcano on Isabela Island in the Galápagos Archipelago of Ecuador from the newly launched
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WorldView-2 sensor. This region was chosen because it includes a mix of land use and cover types situated within an agricultural community comprised of privately held lands, as well as the surrounding protected area managed by the Galápagos National Park Service.
In addition to general categories of LULC that represent the most common land uses and cover types in the study area (built-up, dry grassland, agriculture/grassland, lava rock, bare soil, and forest/shrub), the distributions of two introduced, invasive plants are also mapped: Psidium guajava L. (guayaba or common guava) and Syzygium jambos L.
(pomarrosa or rose apple). In situ LULC data collected in 2008 and 2009 are used to train the classifiers and to assess the accuracy of the classified maps. Tradeoffs between classification accuracy and processing time, training data requirements, and analyst control over
classification parameters with SVM and OBIA are then discussed.