1. Introduction
Mangrove forest is an ecosystem distributed across coastal zones in equatorial and subtropical regions, mainly between the latitudes of 30° N and 30° S [
1,
2,
3,
4,
5]. They are strongly influenced by factors such as temperature, currents, insolation, tides, soil, climate, pH, fresh water supply, and salinity [
6,
7,
8]. Recent studies have indicated that mangrove forests sequester more carbon than rainforests [
9,
10]. Thus, this ecosystem can contribute to carbon emission reduction significantly and can emerge as one of the primary solutions in the global climate change mitigation strategy [
8,
9,
10,
11,
12,
13,
14]. However, global mangrove forest cover decreased by 164,600 ha (1.97%) between 2000 and 2012, with an approximate global loss rate of 13,700 ha or 0.16% per year [
14]. Previous studies have revealed that this ecosystem has been degraded predominantly by land use and cover change (LUCC) [
15,
16,
17,
18,
19].
The mangrove forests that grow throughout Southeast Asia (SEA) extend over 4,000,000 ha and constitute 32.2% of the global mangrove area (
Figure 1) [
20,
21]. Mangrove vegetation in SEA is the most diverse in the world and hosts around 268 plant species [
22,
23,
24,
25]. However, more than 130,000 ha of mangrove forest in this region were lost between 2000 and 2012 to clearing for fuel wood or charcoal production, rice field and oil palm plantation expansion, conversion to aquaculture, and urban development [
25,
26,
27,
28,
29,
30,
31]. Thus, information on mangrove forest loss and its drivers is critical to support decision making by resource managers, planners, and policy makers.
Hamilton and Casey [
30,
31] mapped the mangrove deforestation rate on a global scale. However, their research did not explore deforestation drivers, which vary between regions. Richards and Friess [
30] analyzed the distribution of deforestation drivers in SEA. However, the results generally were only based on dominant land use observations within deforested mangrove forest area interpreted through Landsat images and Google Earth Pro [
30]. It is not efficient to apply this approach to such a wide study area. In addition, the results had only 1° spatial resolution. Further, using advanced remote sensing applications and GIS modeling, several global and multi-temporal datasets and products have been proposed (e.g., cropland, urban, population density, and gross domestic production (GDP)) [
32,
33,
34,
35,
36,
37,
38], which can provide an alternative approach for evaluating mangrove forest conversion in larger areas.
In this study, we examined various environmental and socio-economic data products to contextualize the main anthropogenic drivers for mangrove forest deforestation in SEA: Agriculture, aquaculture, infrastructure, and other human activities. The analysis was conducted in 10 countries: Indonesia, Myanmar, Malaysia, Thailand, the Philippines, Cambodia, Vietnam, Singapore, Brunei, and Timor Leste. The analysis revealed different characteristics of deforestation in each country. The results of this study will provide guidance for decision makers to consider the appropriate policy for preserving or transforming mangrove forests in specific regions.
4. Discussion
4.1. Country Level Analysis
Details of the deforestation area, land use expansion, and deforestation drivers in different SEA countries obtained from this study are listed in
Table 6.
Figure 7 shows that agriculture expansion affected almost all mangrove forests in all the countries. While aquaculture expansion occurred largely in Indonesia, Myanmar, Philippines, Cambodia, and Vietnam, infrastructure expansion was notable in Malaysia, Thailand, Vietnam, and Singapore.
In general, our study revealed that 22.64% of the total deforested area was converted into agriculture, 5.85% was converted into aquaculture, 0.69% was converted into infrastructure, and 16.35% was affected by other human activities. Unfortunately, 54.47% of the land use change in the total deforested area was unobservable; thus, these changes were classified as unidentified. The high percentage of unidentified areas can be ascribed to two factors. Firstly, the difference between the spatial resolution of the deforestation data (CGMFC-21), which was 30 m, and lower resolution of other data products limited the detection of deforestation drivers in small mangrove forest areas, especially in the Philippines archipelago and eastern Indonesia. This limitation was confirmed by the fact that over 90% of the mangrove forest areas in the unidentified areas were less than 6.25 ha, which was the minimum area that could be detected, based on the highest spatial resolution of the data products applied to estimate the drivers (250 m). Secondly, in addition to anthropogenic factors, the role of naturogenic factors must be inspected further.
4.2. Comparison to Other Research
The obtained map was compared to the results of previous research conducted for the same study period by Richards and Friess [
30]. The objectives of the two studies were identical, but the methodologies employed were different. Our research was based on global and multi-temporal datasets, while Richards and Friess’ research was based on dominant land use observations within deforested mangrove areas interpreted through Landsat images and Google Earth Pro [
30]. The current study offers at least three improvements related to the classification model, class information, and spatial resolution. In terms of methodology, the approach used in our study increased the efficiency of the process instead of checking mangrove patches individually. In terms of class information, the integration of socio-economic data facilitated the identification of deforested mangrove forest areas affected by other human activities. In the spatial aspect, the resulting map provided better spatial resolution, i.e., 10 km grid cells.
Figure 8 visually highlights that the mangrove forest deforestation driver types exhibited similar trends in both studies. Our method could also detect rice field expansion in western Rakhine, Myanmar; urban development for tourism in southern Bangkok, Thailand; and fishpond expansion in Central Sulawesi, Indonesia.
Figure 9 provides a country-level comparison and reveals that the two sets of results are similar. According to both studies, the dominant deforestation driver in Myanmar, Malaysia, Thailand, and Timor Leste was agricultural land conversion while that in the Philippines and Cambodia was aquaculture land conversion. However, the dominant deforestation drivers identified in the two studies in Indonesia, Vietnam, and Brunei were different. Nevertheless, the two studies yielded compatible results regarding driver characteristics on a regional level. Agricultural land conversion occurred mostly in Myanmar, Malaysia, and Thailand; aquaculture land conversion occurred mostly in Indonesia, the Philippines, and Cambodia; and infrastructural land conversion occurred mostly in Malaysia, Thailand, and Vietnam. However, the two studies yielded slightly different results especially in terms of the percentage contribution of each driver.
The differences between the two studies can, essentially, be attributed to two main factors. Firstly, mangrove deforestation was calculated using CGMFC-21 data in our study, while the research of Richards and Friess was based on GFC data. Therefore, the numbers of deforested areas could have been different. Secondly, global spatial datasets, which have coarse spatial resolution, were utilized in our study. Thus, it was difficult to acquire precise area information. Nevertheless, a regional perspective was employed in this research and the general roles of various deforestation drivers in the SEA region were captured successfully.
5. Conclusions
Mangrove deforestation in SEA was contextualized by inspecting deforestation drivers using recently developed global environmental and socio-economic data products. Regional analysis revealed that agriculture expansion in mangrove forests occurred in all SEA countries. Aquaculture expansion was noted in Indonesia, Myanmar, Philippines, Cambodia, and Vietnam. Further, infrastructure expansion was observed in Malaysia, Thailand, Vietnam, and Singapore. Overall, 22.64% of the mangrove deforestation in SEA between 2000 and 2012 was attributed to agriculture expansion, 5.85% was attributed to aquaculture expansion, 0.69% was attributed to infrastructure expansion, and 16.35% was not converted to any kind of land use type but was indicated as being affected by other human activities.
However, the deforestation drivers in 54.47% of the converted areas could not be identified. Other anthropogenic factors and naturogenic factors such as abrasion, pollution, sedimentation, water balance, climate issue, pests, and diseases must be acknowledged in future studies to improve the accuracy of mangrove deforestation analysis [
50,
51,
52,
53,
54]. Therefore, additional environmental and socio-economic data products need to be included. Further, the spatial resolution of environment and socio-economic data products applied in this study can be improved. Thus, mangrove deforestation—which is widely distributed spatially and occurs in relatively small areas, as found in many SEA archipelagoes—can be addressed effectively.
The results of this research can facilitate trade-off analysis for natural resources and environmental sustainability policy studies. Diverse management strategies can be evaluated to assess the trade-offs between preserving mangrove forests as climate change mitigation solutions or transforming them for agriculture, aquaculture, and infrastructure to contribute to food security, strengthen commodity exports, and increase economic growth through tourism development.