## 3 Advanced Techniques in Remote Sensing of EBC
### Image Classification  
Regardless of the variety of uses for remote sensing images, the first goal is to extract landscape information from the satellite images [59]. Image classification has been recognized as the most effective means to do so since mid-1800s, when humans first identified different types of land-use and land-cover in aerial photography [60]. [PERSON] discussed in detail the fundamental elements of image interpretation including grayscale tone, color, height and depth, size, shape, texture, pattern shadow, site, association and arrangement [1]. With the widespread of digital computers, special-purpose image classification algorithms have been used to extract land-use/land-cover and biophysical information directly from remotely sensed data [60]. In order to derive more accurate classifications, new approaches have increasingly emerged, and such approaches have made significant contributions to the science of EBC, examples of which would be support vector machines (SVMs), one-class classifier, object-oriented classification, and fuzzy classifications. SVMs consist of many theoretically superior machine learning algorithms, and make use of optimization algorithms to find an optimal separating hyperplane (OSH) between classes based on training samples [61]. The hyperplane is called support vectors [62]. [PERSON] and [PERSON] have demonstrated the robustness of SVMs through comparison with artificial neural networks (ANNs) and machine learning decision trees, especially for small training sets [63]. The SVM was selected by [64] to help understand the relationships among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. [PERSON] _et al._ applied SVM-based evapotranspiration estimation to refine rooting depths for ecosystem modeling in California [65]. Commonly, only one specific class is the foci of research interest [66]. Due to the fact that conventional multiclass classifier may be suboptimal in terms of the classification accuracy of the class of interest, a one-class-classification approach was suggested to focus tightly on the specific class. For example, [PERSON] _et al._ applied the one-class classifier based on the support-vector data description (SVDD) to map fenland habitat in support of conservation activities [66]. An accuracy of 97.5% and 93.6% from the user's and producer's perspectives was obtained, and it performed much better than conventional maximum-likelihood classification. In the same year, the classifier was used to map and monitor coastal saltmarsh habitats of high conservation value under the European Union's Habitats Directive [67]. With the wide availability of high-spatial resolution satellite data, pixel-based classification algorithms seem not to be ideal to extract information desired from the data exhibiting high frequency components with high contrast and horizontal layover of objects [60]. Therefore, object-oriented classification algorithms have been developed to meet this need, and have established improved classification accuracy when compared with the traditional methods [5, 60]. The basic processing units of object-oriented classification are segments, so-called image objects that represent a relatively homogenous unit on the ground [68]. Then classification was performed on image objects, and not on pixels. One of the most popular algorithms was developed to the software of Definiens' Developer (also called eCognition; [69]). Advantages of object-oriented classification are to make full use of meaningful statistic and texture calculation, uncorrelated shape information (e.g., length-to-width ratio, direction and area of an object, _etc._) and topological features (neighbor, super-object, _etc._), and theclose relation between real-world objects and image objects [68]. [PERSON] _et al._ pointed out that the advantages include rapid process, and scale flexibility in which users can select different scale levels according to their images [60].