**1. Introduction**

Mangrove forests are located along sloping shores, river estuaries, deltas, bays influenced by tides, and generally found in tropical and subtropical areas [1,2]. As a defense for shore and marine ecosystems, mangroves are an essential link to maintaining the waters' biological cycle [3,4]. Mangrove forests have several benefits, among others, as a carbon storage [3,5], prevent abrasion [5], reduce the impact of tsunamis [6] and as habitat breeding fish [7].

Indonesia is a country that has the largest mangrove forest in the world, reaching 59.8% of the total area of mangrove forests in Southeast Asia [8]. The area of mangrove forests in Indonesia is around 4.5 million ha with the proportion (18–23%) exceeding Brazil (1.3 million ha), Nigeria (1.1 million ha), and Australia (0.97 million ha) [9]. South Sumatra Province is one of the provinces in Indonesia which has widespread mangrove forests. Based on the results of the inventory and description of mangrove forests implemented by the Musi Watershed Management Center in 2006, the area of mangrove forests

in South Sumatra province is around 1,693,110.1 ha [10]. It is also consistent with the Decree of the Minister of Forestry Number 95/Kpts-II/2003 dated March 19, 2003, which declared that South Sumatra has a mangrove area of 202,896.3 ha, specifically in Sembilang National Park [11]. Sembilang National Park is dominated by mangrove forests due to its position on the coast of the Banyuasin peninsula. However, to the West and Northwest of Sembilang National Park, there is a large stretch of peat swamp forest which is an extension of the peat forest in the Berbak region of Jambi Province [12]. The condition of mangroves in Indonesia, especially in the National Park, is experiencing tremendous pressure, both from human activities and environmental factors [13]. Generally, the destruction of mangrove forests is caused by building materials, animals feed and forest fire [14].

The forest fire occurred in Indonesia during the dry weather in 1997. Firstly, forest fires in Indonesia were caused by human activities, such as: cultivation of deliberate slash and burn by farmers on peatland areas, land conversion, fishing, and logging, nevertheless, the extent of the respective causes are unknown [15,16]. Then, the fire quickly burns dry organic matter so that spreads over a large area and caused mega fire. Mega forest fires started in Southern Sumatra and Southern Kalimantan in early May and continued until the second week of November 1997 [17]. Mega forest fires in Indonesia have also been triggered by the El Niño climate phenomenon [18]. It is thought that during the 1997 El Niño fires in Indonesia, between 0.8 and 2.6 Gt of carbon was released into the atmosphere as a result of burning peat and vegetation. This amount is equivalent to 13–40% of global and carbon emissions [19,20]. On the other hand, this disaster also affected the health of the population in Sumatra, Kalimantan, and neighboring countries, and disrupted political stability [21]. Approximately 35 million people in Southeast Asia were affected [22]. The cost of smog pollution costs around USD 674–799 million and is associated with carbon emission losses of around USD 2.8 billion [23]. This occurrence was declared to be one of the worst environmental disasters of all time [24].

According to Figure 1 many fires started since early May 1997. A wave of land clearance fires moved from the north to the south of the island. Fire numbers peaked in Aceh, North Sumatra and Riau provinces in May and including July was the large number of fires, in Jambi from July to September, in South Sumatra in September [25]. September was the primary month of forests fire in South Sumatra [15,25,26]. The most significant concentrations were in four regions, there were Pampangan district, between Palembang city and Jambi province border, Pendopo district in the central-western part of South Sumatra, and Jambi east near to Berbak National Park and in Lampung province from August to October [27]. The number of hotspots decreased slightly at the end of October, and then, over two to three days starting from 6 November, all major fires discontinued, apparently after heavy rain [27]. All significant fires correlated with the lowest month of rainfall in each province, because the habit of seasonal rainfall controls the tendency for fires to occurred in Sumatra and is strongly influenced by the striking differences in land use types in each of the eight provinces. Despite significant regional differences in average annual rainfall in Sumatra, the climate is almost humid, and 85 per cent of the island has a dry season (mean monthly rainfall less than 100 mm) of less than two months [28].

**Figure 1.** Distribution of hot spot in Sumatra 1997 [25].

The total area affected by the fire in Indonesia appears to have been excess of two mile ha, including Sumatra [29]. The main areas affected by forest fires in Sumatra is Sembilang National Park. Therefore, the Indonesian governmen<sup>t</sup> keeps on trying to rehabilitate the mangrove forest in Sembilang National Park as the largest mangrove forest in Sumatra through publish policies related to forest fires by issuing rules and regulations relating to the prevention and control of forest fires which are regulated in Law no. 6 of 1990, Law No. 5 of 1994, Law No. 23 of 1997, Law No. 41 of 1999 and Government Regulation No. 4 of 2001 [30]. These regulations consist of prevention and control through coordinated extension activities, the prohibition of burning activities, improvement the skills of human resources both from the governmen<sup>t</sup> and companies, and compliance, and procurement of fire-fighting equipment. Hence, the condition of the mangrove forests in the Sembilang National Park is very dynamic and changed every year. Mangrove forests need to be observed to control and rehabilitate. Moreover, the future of land use is also needed to support planning and policies [31]. This aspect can be approached through land cover change modeling as an instrument to support the analysis of the causes and consequences of land cover change.

Modeling land cover change depends upon the accurate extraction of both past and present land cover information [32], which the past and future scenarios are evaluated by model. Remote sensing has been widely proven to be essential in providing information regarding the land cover change [33,34], in which most studies use pixel-based image analysis methods [35–37]. Generally, the algorithm used is the maximum likelihood [38,39] and then the land cover change is evaluated and assessed by map algebra [40,41]. Meanwhile, another method used for predicting land cover change is based on multivariate analysis through image regression [42]. However, the limitations of this model are cannot quantify the change and aim to observe the temporal analysis [43]. Therefore, this condition takes Markov-Cellular Automata to overcome these limitations. Markov-Cellular Automata is an efficient, simple model and has an excellent ability to simulate and predict land cover change based on spatial data [44–46]. The Markov-Cellular Automata model is an integration of the Markov Chains and Cellular Automata models. The Markov Chain is a statistical model used to determine the probability (probabilistic) of change for each land class from two land data sets at different periods [47,48], while the Cellular Automata model is expressed as an automaton (raster data cell), which is having cell contents that can change or transfer at any time, according to the transition rules that are recognized in each cell [49–52]. The Markov-Cellular Automata model is a good application for identifying and predicting land cover change because it estimates spatial and temporal components [53].

Moreover, the application of a suitable classification algorithm is essential. Various classification algorithms have been developed for mangrove mapping such as ISODATA [54], Maximum likelihood [55–58], object based classification [59,60], and support vector machine [61–63]. Support vector machine was a reliable machine learning algorithm that provides acceptable accuracy for mangrove mapping [64,65]. Madanguit et al. (2017) [64] compared the support vector machine and QUEST classification algorithms for mangrove mapping. The results showed that the support vector machine algorithm provides higher accuracy than QUEST with 94.9% and 93.6%. Firmansyah et al. (2019) [65] also said that support vector machines could minimize mangrove mapping errors compared to decision trees. Feng et al., 2016 [63] calculated and simulated urban development in Shanghai-China using a machine learning-based Markov-Cellular Automata integration model. The support vector machine algorithm was used and compared with the conventional algorithms. His research results had indicated that the conventional algorithm was not good enough to simulate the complex boundaries between urban and non-urban areas, and the support vector machine algorithm provides accurate results. Referring to several previous studies, the objectives of this study are:


The model uses Markov-Cellular Automata based on a support vector machine algorithm. It is assumed that it can be used by a study to establish policies, especially in anticipating negative impacts on environmental changes and mangrove planning and managemen<sup>t</sup> purposes.

#### **2. Materials and Methods**
