**3. Results**

#### *3.1. Land Cover Classification in 1989, 1998, 2002, and 2015*

The land cover classification was obtained from Landsat 5, Landsat 7 ETM+, and Landsat 8 OLI Images in 1989, 1998, 2002, and 2015 using the support vector machine algorithm in Sembilang National Park. Land cover classes were categorized into three: Mangroves, Non-mangroves, and Water.

We obtained the land cover map from the classification algorithm by use of the support vector machine (Figure 7). We identified three dominant classes in the study area including Mangrove, Non-mangrove, and Water. Based on Figure 7b, for the years 1989 to 1998 we can see visually, there was the decrease of Mangrove forest because of forest fire, while, from the years 1998 to 2015 there was the significant increase of Mangrove forest. The accuracy assessment of classification obtained by each year can be seen on Tables 3–6.

**Figure 7.** Land cover map as classification result in (**a**) 1989, (**b**) 1998, (**c**) 2002, (**d**) 2015.



**Table 4.** The confusion matrix of support vector machine classification in 1998.



**Table 5.** The confusion matric of support vector machine classification in 2002.

**Table 6.** The confusion matric of support vector machine classification in 2015.


The confusion matrix shows accuracy assessment of classifications for the years 1989, 1998, 2002, and 2015 (Tables 3–6). The classification provided from the use of training data indicated that land cover reliability mapped, showed that all categories had above 90% rate of overall accuracy and kappa statistic. The discrimination of mangrove class also shows good accuracy with a commission error in years 1989, 1998, 2002, and 2015 of 0.4%, 2.8%, 3.2%, and 1.3%, respectively. In addition, the commission error in 1989, 1998, 2002, and 2015 of 0%, 0.9%, 1.3%, and 0%, respectively.

#### *3.2. Land Cover Change Classification*

The support vector machine classification produces an area of land-use classes (Mangrove, Non-mangrove, and Water). Overall, each land-use class area for years 1989, 1998, 2002, and 2015 can be seen in Table 7.


**Table 7.** Area Land-use (years) 1989, 1998, 2002, and 2015.

The area occupied by each class in 1989 was: Mangrove 58,145.5 ha (26.2%), Non-mangrove 53,265.4 ha (24.1%), and 109,886 ha (49.6%). In 1998, Mangrove area was decreased to 36,847.4 ha (16.6%), Non-mangrove area was increased to 73,327.4 ha (33.1%) and the Water area was increased to 111,112 ha (50.2%). In contrast to 1989, mangrove areas show increased trends both in 2002 and 2015. In 2002, that the Mangrove area was increased to 55,548.3 ha (25.1%), Non-mangrove has decreased to 58,419.1 ha (26.3%), and the water area has decreased to 107,329 ha (48.5%). In 2015, the Mangrove

areas has increased to 60,697.5 ha (27.4%), Non-mangrove areas has decrease to 51,965.8 ha (23.4%), and the Waters area has increased to 108,633 ha (49.1%).

#### *3.3. Transition Matrix and Transition Probability Matrix for the Land Cover*

The transition matrix area and transition probability matrix for years 1989–1998, 1998–2002, and 2002–2015 can be seen in Tables 8 and 9. The transition probability matrix from 1989 to 2015 then used to predict mangrove forest change in the year 2028. Validation of Markov-Cellular Automata identify map was carried out by comparing prediction Mangrove forest map of the year 2028 with the support vector machine 2015 classified Mangrove forest map.


**Table 8.** Transition Probability matrix in 1989–1998, 1998–2002 and 2002–2015.

**Table 9.** Transition matrix area in 1989–1998, 1998–2002 and 2002–2015.


The transition probability matrix for years 1989–1998, 1998–2002, and 2002–2015 show that changing mangroves to non-mangroves from 1989 to 1998 is 0.3 and probability decreased by 0.2 in 1998–2002 (Table 8). When transition of non-mangrove into mangrove was observed, the transition probability was very low in 1989–1998 of 0.1. The probability of changing non-mangroves to mangrove areas in 2002–2015 decreased by 0.2. Mangrove, non-mangrove, and waters classes for the period 1989–1998, 1998–2002, and 2002–2015 have a probability value above 0 can change to the other classes, indicating the possibilities appropriate for analyzing changes in existing land cover.

Land-cover change was shown as transition matrix area in 1989–1998, 1998–2002 and 2002–2015 (Table 9). The probability transition shows that from 1989 to 1998, mangroves had the highest chance of becoming non-mangroves with a prediction of pixel allocations of 142,756 pixels or equivalent with 12,848.4 ha (area pixels = 900 m2). In addition, in the same period, waters had the most significant chance of becoming mangrove with the prediction of pixel allocations of 141,087 equivalent with 12,697.8 ha. The 1998–2002 period shows that mangroves, non-mangroves, and water have the highest chance of becoming changed with several pixel allocations.

#### *3.4. Prediction of Land Cover Change in Year 2002 and 2015*

The simulation results of the Markov-CA model to predict land cover in 2002 and 2015 were shown in Figure 8, while the area of land cover prediction map in 2002 and 2015 can be seen in Table 10.

**Figure 8.** Land cover prediction map in (**a**) 2002 and (**b**) 2015. **Table 10.** Land cover area of prediction map in 2002 and 2015.


The area occupied by each class in 2002 (prediction) was: Mangrove of 42,224.1 ha (19.1%), Non-mangrove of 77,012.6 ha (34%), and Water of 10,059.9 ha (46.1%) (Table 10). In contrast, 2015 (prediction), Mangrove area was increased to 86,245.2 ha (38.9%), while both Non-mangrove area and Water were decreased to 47,087.1 ha (21.2%) and 87,964.2 ha (39.7%), respectively.

#### *3.5. Kappa Index Agreement*

Kappa evaluate how well classification or modeling performs excluding chance agreemen<sup>t</sup> [93]. In this study, kappa was used to assess the agreemen<sup>t</sup> between the 2002 and 2015 actual land cover maps and simulations. Kappa index agreemen<sup>t</sup> of prediction years 2002 and 2015 can be seen in Table 11. We found that the lowest kappa index agreemen<sup>t</sup> of 2002 (prediction) is 0.7 as *Kstandard*, while the highest kappa index agreemen<sup>t</sup> of 2002 is 0.8 as *Klocation* and *KlocationStrata*. In addition, the lowest kappa index agreemen<sup>t</sup> of 2015 (prediction) is 0.7 as *Kstandard*, while the highest kappa index agreemen<sup>t</sup> of 2015 (prediction) is 0.8 as *Klocation* and *KlocationStrata.* According to Gwet (2014) [95], *Kno* and *Kstandard* obtained from both prediction map in 2002 and 2015 indicated substantial agreement, while *Klocation* and *KlocationStrata* obtained from both prediction map in 2002 and 2015 indicated almost perfect agreement.

**Table 11.** Kappa Index Agreement in prediction years 2002 and 2015.


#### *3.6. Prediction of Land Cover Change in 2028*

Land cover change years 2028 prediction using land over obtained from land cover year 2015. The 2015 land cover data were used for the base map, the potential transition map, and a transition area matrix for 2002–2015. The future land cover models are predicted, as shown in Figure 9 while the prediction area for 2028 is seen in Table 12.

**Figure 9.** Prediction map for 2028.

**Table 12.** Land cover change prediction.


Table 12 shows the period between 2015 and 2028, area of Mangroves increased from 27.4% to 31% or 7974.8 ha, while the area of Non-mangrove also increased from 23.4% to 25.1% or 3725.3 ha, and Water area decreased from 49.1% to 43.8% or 11,696.7 ha. In general, the area of classification results in 1989, 1998, 2002, and 2015 and the predicted results for 2028 can be seen in Table 12.
