*4.2. Accuracy Assessment*

The su fficient number of training data and the selection of classification approaches are essential factors in successful classification [99,100]. In this study, training data were collected from more acceptable spatial resolution imagery. We collected a total of 1042 training pixels covering the Sembilang National Park of the area mapped. In this study, the amount both of the training and testing used are unbalance data. There are 432 training pixels with the primary class of mangrove, 333 training pixels labeled as land, and 277 training pixels labeled as water. The number of training data was following [101]. The training data size should not be smaller than 10 to 30 times the number of bands for each object. All pixels were selected randomly following to uniform in ground truth data. According to some researchers, balancing samples in classification is a controversial topic [102–104].

In some cases, unbalanced data was inevitable due to the complexity and heterogeneous landscapes in the study area when choosing training data [105]. Therefore, this problem can be handled using the appropriate classifications approach, such as a vector machine. The support vector machine for handling unbalanced data in the classification process is the best choice, proven in high accuracy (Table 14).


**Table 14.** The classification accuracy.

The classification accuracy obtained from Landsat 1989, 1998, 2002, and 2015 indicated that the support vector machine was the right option for mangrove mapping based on an unbalance training sample. In this study, the overall accuracy of the land cover maps for 1989 (99.8%), 1998 (98.5%), 2002 (97.9%), and 2015 (99.5%) were achieved. All accuracy indicators of overall accuracy, kappa statistics, Mangrove user accuracy, and Mangrove producer accuracy were above 90%. Table 14 shows that the support vector machine classifier's performance can dramatically decrease with a relatively small number of mislabeled examples [76]. According to Mountrakis (2011) [76], support vector machines are not relatively sensitive to training sample size, and some literature has improved support vector machines to work successfully with limited quantity and quality of training data [76].

#### *4.3. Matrix Probability Transition*

The probability matrix is a factor that sets the trend of change in surrounding cells as a function of cell conditions themselves. The CA-Markov was applied for simulation of mangrove cover changed. Three intervals period were used in this study, including an interval of 9 years (1989–1998), an interval of 4 years (1998–2002), and 13 years (2002–2015). Each interval represents each land cover category projections, while the third interval is determined by the results of project accuracy in 2028.

The probability transition matrix had been done for the interval in 9 years (1989–1998), an interval of 4 years (1998–2002), and 13 years (2002–2015) (Figure 10). Figure 10, the interval of 9 years (1989–1998), indicated that more than half of the Mangrove was changed into Non-mangrove areas (34.8%). This result was related to the Forestry Research and Development Agency (2013) that South Sumatra experienced relatively high deforestation during the 1990, resulting in a decrease in mangrove forest cover due to forest fires in 1997–1998. The worst of them occurred in 1997 during the dry weather fostered by El Niño [29]. At the same time, there was no change in Mangrove becoming Water. In the same interval years, 82% of Non-mangrove remained unconverted. So, the water 84.8% remained unconverted. The four-year (1998–2002) interval showed a positive change concerning the Mangrove forest recovery, which is showed 66.4% Non-mangrove and 8.3% Water shifted to mangrove areas. The increasing Mangrove areas resulted in better managemen<sup>t</sup> by the governmen<sup>t</sup> in Sembilang National Park. It is also proven from 13 years (2002–2015), indicating that 81.1% of Mangroves remain unconverted. Totally 22.3% Non-mangrove and 7.2% Water changed to Mangrove areas. The increase of Mangrove areas indicated better managemen<sup>t</sup> in Sembilang National Park.

**Figure 10.** The probability matrix: ( **A**) an interval of 9 years (1989–1998), (**B**) an interval of 4 years (1998–2002), and ( **C**) an interval of 13 years (2002–2015).

#### *4.4. Land Cover Area in Years 1989, 1998, 2002, 2015 and Predicted 2028*

The statistics area (ha) trend was derived from the support vector machine classification for 1989, 1998, 2002, 2015, and prediction 2028 (Figure 11). In 1989–2018, the trend of mangrove land cover fluctuated indicated the lowest mangrove area is in 1998 of 36,847.4 ha, and the highest mangrove area predicted in 2028 of 68,672.3 ha. In the interval years of 1989–1998, the loss mangroves in Sembilang National Park are 21,298.1 ha. The decrease of mangrove was caused by the worst forest fires in Indonesia in 1997 [29]. However, in 1998–2028, the increase of mangrove forest became 55,548.3 ha in 2002, 60,697.5 ha in 2015, and 68,672.3 ha in 2028. The highest rate of increasing mangrove forests was in 1998–2002 of 8.4%. The increase of the mangrove forest area indicated that the governmen<sup>t</sup> had succeeded in mangrove rehabilitation management.

**Figure 11.** Area statistics (ha) of land cover class for the years 1989, 1998, 2002, 2015, and predicted 2028.
