*2.4. Classification*

The support vector machine was used for classification of mangrove forests in study area. This classification was applied to the Landsat 5 images in 1989, Landsat 5 in 1998, Landsat 7 ETM+ in 2002, and Landsat 8 OLI images in 2015. This study was used support vector machine to fit an optimal separating hyperplane or set in a high or infinite-dimensional space to locate the optimal boundaries between classes. In this case the 3 classes defined previously there are mangroves, non-mangroves and water (Table 2).



Based on statistical theory, support vector machines operate by classifying two or more classes by studying for the best hyperplane that utilizes data at the separation point (super vector) even for a limited number of samples [74,75]. The support vector machine equation can be seen in the following Equation (1):

$$\text{SVM}\_{\text{(F,l)}}(\mathbb{R}) = \text{sign}\left(\sum\_{i}^{N} y\_i \alpha\_i (f\_{\mathbb{R}}.f\_i) + b\right) \tag{1}$$

where the SVM trainer of *R* is the class of region based on specific feature type F and specific scale λ. *yi* is the support vector class and α*i* (*i* = 1, ... , *N*) is decision coe fficient with *N* is total of region. The support vectors are the *fR* is the feature vector the region and *fi* such that α*i* > 0, and *b* is a parameter found during the training.

#### *2.5. Training Data Collection Scheme*

The training data were based on two di fferent acquisition methods and incorporate field survey image interpretation using satellite data. Considering the time and access to the area is not always possible, GPS measurements were also impossible. The majority classes in Sembilang National Park where is not built-up area, the land cover only mangrove, non-mangrove (such as brush area), and water area. The distribution of training data can be seen in Figure 6. This study, mapping mangrove forest using Landsat 5, Landsat 7 ETM+, and Landsat 8 OLI images which spatial resolution is 30 m therefore training data of mangrove, non-mangrove and water patched on the field is more than 0.009 ha.

**Figure 6.** The distribution of training data.
