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Article

Causes and Effects of Mangrove Ecosystem Damage on Carbon Stocks and Absorption in East Java, Indonesia

by
Rudianto Rudianto
1,*,
Dietriech G. Bengen
2 and
Fery Kurniawan
3,4
1
Study Program of Marine Science, Department of Utilization of Fisheries and Marine Resources, Faculty of Fisheries and Marine Sciences, Brawijaya University, Jl. Veteran No. 16, Ketawanggede, Lowokwaru, Malang 65145, Indonesia
2
Department of Marine Science and Technology, Faculty of Fisheries and Marine Sciences, IPB University (Bogor Agricultural University), Jl. Agatis Darmaga Bogor, Bogor 16680, Indonesia
3
Department of Aquatic Resources Management, Faculty of Fisheries and Marine Sciences, IPB University (Bogor Agricultural University), Jl. Agatis Dramaga Bogor, Bogor 16680, Indonesia
4
Centre for Coastal and Marine Resources Science Studies (CCMRS), IPB University (Bogor Agricultural University), Jl. IPB Baranangsiang No.1, Bogor 16129, Indonesia
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(24), 10319; https://0-doi-org.brum.beds.ac.uk/10.3390/su122410319
Submission received: 8 October 2020 / Revised: 3 December 2020 / Accepted: 7 December 2020 / Published: 10 December 2020
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Abstract

:
The mangrove ecosystems in East Java are widely exploited and converted for other land. Mangrove forests help decrease carbon dioxide concentration, are very efficient carbon sinks and store them in large quantities in biomass and sediments for a long time. This paper tries to understand the causes and effects of mangroves ecosystem damage on carbon stocks and absorption, with East Java, Indonesia as a case study. The Driver, Pressure, State, Impact and Response (DPSIR) framework, Analytic Hierarchy Process (AHP), and Partial Least Squares (PLS) used to identify and solve these problems. The result shows that the destruction of mangrove forests in East Java has occurred due to land conversion intensification. Accordingly, the mangroves’ average carbon sequestration and storage in East Java can be classified as moderate to low. Illegal logging is the leading cause, so there needs to be a clear policy that involves the government and the community. This result suggests that forming a strategy to prevent illegal logging and increase carbon sequestration and storage must be carried out, and community engagement in decision-making processes to protect and manage the mangrove forests.

1. Introduction

Mangroves, as a coastal ecosystem found in tropical and subtropical areas, store carbon. In the last few decades, mangroves have experienced an increasingly rapid loss rate due to land-use changes. Habitat conversion changes vary globally. The causes of habitat conversion changes include conversion to cultivated areas, expansion of agriculture, over-exploitation of forests, construction of industries and upstream dams, dredging, eutrophication of overlying water, and urban development [1].
The carbon in the wetland soils is buried for thousands of years and does not reach saturation [2]. This carbon is different from the carbon present in freshwater systems and generally does not produce much methane gas. Coastal carbon is trapped by sediment and plays an essential ecological role, including carrying limited nutrients [3] and helping coastal habitats adapt to sea-level rise [4]. Thus, coastal areas are a strategic route to capture and store carbon [5]. The depletion of mangrove cover decreases the ability of mangroves to store and absorb mangroves [6]. For various purposes, logging of mangroves, both licensed and unlicensed, is damaging from an ecological perspective [7].
Indonesia has mangrove biodiversity, representing around 22.6% of the world’s total mangrove ecosystems [8]. Therefore, Indonesia is a country with the most extensive mangrove forests in the world [9]. Mangrove forests are ecological systems in coastal and estuarine areas that receive nutrients and sediment from terrestrial environments, but deforestation and mangrove degradation continue [8,10,11,12,13]. Supposedly, the mangroves are not managed correctly. In that case, it will soon only become a part of history [14]. Rahadian, et al. [15] reported that the diversity of mangrove area data is a national problem given the importance of historical data on reliable and consistent mangrove forests. The reliability of historical data on mangrove forests is essential for monitoring and supporting the development of mangrove management strategies.
Indonesia has lost mangrove forests of about 600,000 ha for shrimp farms [16]. Mangrove area reduction also occurs in the East Java province, although there are variations in data from several existing sources. This situation is very confusing [15]. In the 1960s, the mangrove forest was recorded as 5000 ha; however, it was decreased by 500 ha in 1987 [17]. Burbridge and Koesoebiono [18] stated that the mangrove forest area in East Java in 1982 was 6000 ha, equal to the mangrove forest area in 1978, although the interval was four years. Soegiarto and Sukardjo [19] confirmed that the mangrove forest area in East Java was the same as that in another research covering 6000 ha [18]. RePPProT [20] reported that the mangrove forest area was 7500 ha in 1985–1989, which was relatively the same size as in the research done by Naamin [21] in 1986 and Choong, et al. [22] in 1990. The data presented by BIG [23] showed that the mangrove forest area was 18,253.82 ha in 2012, whereas the data by KLHK [24] showed that the mangrove forest-covered 71,708.20 ha in 2011. The one map policy by Susanto, et al. [25] stated that mangrove forest data in East Java covered 21,944 ha in 2016.
This situation greatly influences the management direction and policies needed and the calculation of carbon value in mangrove ecosystems, which is the main argument in mangrove ecosystem management. Therefore, this paper tries to understand the causes and effects of mangroves ecosystem damage on carbon stocks and absorption, with East Java, Indonesia as a case study.

2. Materials and Methods

2.1. Study Area and Data Collection

The total land area of coastal regency in Java Province is 3,487,525 ha, namely regency of Sumenep, Pamekasan, Sampang, Bangkalan, Tuban, Lamongan, Gresik, Surabaya, Sidoarjo, Pasuruan, Probolinggo, Situbondo, Banyuwangi, Lumajang, Trenggalek, Pacitan, Blitar, Tulungagung, and Malang [26]. The population of East Java Province in 2019 is 39,699,000 people, with a population density of about 826.39 people/km2. The population of coastal regency in 2019 is 27,681,000 people with population density about 34,736 people/km2. This study was conducted in four sites, including the coastal of Lamongan Regency (CLR), Penunggal Village, Pasuruan Regency (PVP), Alas Purwo National Park (APP) Banyuwangi Regency, and Clungup Coastal, Malang Regency (CCM), East Java Province (Figure 1).
Primary data were obtained by field observation and interview methods using questionnaires. The questionnaire is made in two forms, namely open and closed questionnaires. Open questions are asked for perceptions of the existing situation and changes, forms of use and protection measures, and problems. Meanwhile, closed questions were made to assess the level of problems and responses required using the Saaty scale. Interviews were conducted on 30 selected respondents at each location by five categories, including local governments (5 persons), non-fishermen (5 persons), private sectors (5 groups), fishermen (8 persons), and local non-government organizations for conservation (7 organizations). Respondents selected are experts and understand the exact conditions of the study location, mangrove ecosystem and carbon issues. The respondents’ criteria include having a formal education degree in the field of environment and natural resources, or mangrove conservation actors, or users and beneficiaries of mangrove ecosystems, with more than five years of experience. Secondary data were obtained from East Java Province in figure year 2008–2019 for population growth, the population of coastal regency, and production of logs [26,27,28,29,30]. The land covers recalculation from satellite imagery year 2008–2019 [31,32,33,34,35], and scientific article.

2.2. Descriptive and Statistical Analysis

Data were tabulated for validation and categorized. Driver, Pressure, State, Impact, and Response (DPSIR) approach used to identify, analyze, and evaluate the factor of ecosystem mangrove loss in East Java Province, both qualitative and quantitative data. The frequency of issues arising is considered a major factor in each of the DPSIR categories. DPSIR is related to ecosystem degradation influenced by humans and used by the manager to make a decision [36]. Sarmin, et al. [37] used this approach to recognize and estimate the mangrove deforestation impacted by anthropogenic factors. Adams and Rajkaran [38] used DPSIR to understand the manager and government’s response management. DPSIR can describe a socio-economic and cultural factor that impacted the mangrove ecosystem loss. The pressures are population growth and coastal development. So, it impacts the area, density, and species diversity of mangroves.
The anthropogenic destruction of mangrove forests, primarily through illegal logging, needs to be formulated using the Analytic Hierarchy Process (AHP) [39] and Partial Least Squares (PLS) [40] models. The AHP is used to formulate a sustainable mangrove forest management policy to increase carbon sequestration and stock. Decision-making using AHP is done by detailing the problems, which are grouped into components. Then, the components are formed into a hierarchy. The hierarchy at the very top is derived into several other elements of the set. The element assessment obtained from interviews with experts was carried out by determining the weight using the pair comparison method. Processing is done using a consistency ratio of 0.1 with the amount of consistency; the key person’s decision based on the priority scale is said to be quite consistent. It can be implemented as policy direction and management. Meanwhile, the PLS method is a multivariate statistical technique that can handle many response variables and explanatory variables. The AHP and PLS methods are operated to find the main factors of illegal logging, including the best solution, as the basis for the required responses. Those analyses are a better method of multiple regression analysis and principal component regression because this method is more robust or invulnerable.
Five facets were used to examine the AHP and PLS method, i.e., irrational use, illegal logging, weak carbon sequestration, and the effect of forest damage on both carbon sequestration and law enforcement. Facets are obtained and arranged based on the problems identified from the preliminary observations and literature study, including Pendleton, Donato, Murray, Crooks, Jenkins, Sifleet, Craft, Fourqurean, Kauffman, Marba, Megonigal, Pidgeon, Herr, Gordon and Baldera [1], Chen, Chen, Yu, Tam, Ye and Chen [2], Sasmito, Taillardat, Clendenning, Cameron, Friess, Murdiyarso and Hutley [6], Giesen, Wulffraat, Zieren and Scholten [9], Hartati and Harudu [11], Richards and Friess [12], Ilman, Dargusch, Dart and Onrizal [16], Burbridge and Koesoebiono [18], Naamin [21], Budianto, et al. [41], Donato, et al. [42], Heriyanto and Subiandono [43], Kridiborworn, et al. [44], Murray, et al. [45], Primavera [46], Sejati, et al. [47], Slamet, et al. [48], Turschwell, et al. [49], Yang, et al. [50], Yatim and Hoi [51], Sulaiman, et al. [52], Aksornkoae [53], Kustanti, et al. [54], Alemu I, et al. [55], Arumugam, et al. [56], Beymer-Farris and Bassett [57], Dahdouh-Guebas, et al. [58], Glaser, et al. [59], Moschetto, et al. [60], Onyena and Sam [61], Ounvichit and Yoddumnern-Attig [62], Rasquinha and Mishra [63], Sharma, et al. [64], Triyanti, et al. [65], Veettil, et al. [66], Yu, et al. [67]. Details of the problem are grouped into components, which are formed into a hierarchy. The top hierarchy is lowered into several other set elements to get more specific elements that will be controlled in the recommended strategy and management. Nevertheless, government regulation, politics, and culture were not considered in this analytical problem.

3. Results

The status of the mangrove ecosystem in East Java Province, Indonesia, has high complexity (Figure 2), both for drivers, pressure, state and impact components. Thus, it requires an appropriate response to eliminate drivers, reduce stress, restore the status and mitigate impacts.

3.1. Driver

Generally, the driver of mangrove damage is population growth [37] and land use for aquaculture [16]. Population growth is a very significant influence the mangrove deforestation. Humans need room for home and economic activities for their livelihood. Human activities that influence deforestation are fishing, forest logging, coastal development, coastal engineering, aquaculture, tourism, recreation, and mining [68].
Figure 3 shows that the mangrove area from 2008 to 2019 is related to population growth for all regencies and coastal regencies in East Java Province. The presentation declined by 79.47% for 28 years. The mangrove area in 1991 is 57,500 ha [69], which decreased to 11,800 ha in 2019. The number of populations in all regencies and coastal regencies increased from 2008 to 2019, about 1.07%. The number of populations in coastal regencies in 2008, about 25,683,752 people, increased to 27,681,000 people in 2019. Population growth was linked with land-use needed, especially for residential, which increased from 390,000 to 507,700 ha in 2008–2019 or 0.8 to 1.2%. Population density in coastal regency increased from 32,626 to 34,736 people/km2 in 2008–2019.
Generally, Aquaculture or “Tambak” is one factor that influenced mangrove deforestation in East Java Province in the last five years. The Ministry of Forestry of Indonesia estimates that Indonesia’s mangrove area declined at 100,000 ha a year in 2006. Correspondingly, aquaculture area increased from 47,913 to 86,800 ha in 1991–2008 and declined to 31,300 ha in 2014 [69]. In 2014, the first increase in the aquaculture area was 46,200 to 73,000 ha in 2019. It impacted the Ministry of Marine Affairs and Fisheries of Indonesia regulation about shrimp production projection for ten years in Presidential Regulation Number 2 of 2015 on Medium Term Development Plan. Some program is already used to increase the production of shrimp from 300,000–400,000 t to 600,000–1,000,000 t [16]. The scenario to reach this production is chosen by open new aquaculture areas, so farmers clear the mangrove. Moreover, opening a salt pan can impact mangrove existence, although it is used interchangeably with fish ponds. These two policies show that there are programs that are not in line and negatively impact sectoral activities.

3.2. Pressure

Globally, land-use change is influenced by human activities that are impacted by population growth and an important threat for mangroves [10]. Human activities can increase building, agriculture, and forest deforestation [70,71]. Land-use using for residential is very significant to improve economic and land prices [50,72]. This land is used to open the new aquaculture and industry [16,47]. Richards and Friess [12] showed that mangrove conversion used for aquaculture (29.9%), agriculture/rice (21.7%), oil palm (16.3%), mangrove forest (15.4%), urban (4.2%), and others category (12.3%) in Southeast Asia. Aquaculture in East Java Provinces increases until 2019 and probably would grow because humans understand shrimp ponds’ economic value.
Timber production of logs in East Java Province is 3,351,598 m3 in 2014, declined to 3,229,887 m3 in 2016, and increased to 3,912,134 m3 in 2019 (Figure 3). Timber production is significantly related to mangrove deforestation in East Java Province. Monitoring and giving a quota impact on illegal logging. Illegal logging occurs due to clearing for aquaculture, industry [47], port, agriculture, firewood, and charcoal [38]. Instead, the mangrove charcoal is the best charcoal with the best temperate than other wood [51]. It is an essential material for purification, color removal, adsorbent, and catalyst [41,44].

3.3. State

Java Island lost about 800,000 ha of mangrove forest for thirty years [16]. Specifically, East Java Province lost about 38,100 ha of mangrove forest between 1991–2019 (Figure 1a). The mangrove deforestation occurs in some coastal regencies, including Panunggal Village, i.e., 11.62 ha in 2013, 17.83 ha in 2015, and 12.52 ha in 2018 [73], and Clungup Malang Regency, i.e., 27.45 ha in 2015 and 25.65 ha in 2018 [74]. Hidayah and Wiyanto [75] reported that the mangrove area in Sidoarjo Regency declined about 33.07 ha in 2002–2010. Mangrove forests in two locations in East Java Province were selected as the research setting. East Java Province was chosen because almost half of the mangrove forests had been damaged. Data by Susanto, Subarya and Poniman [25] compared with Usmawati [76] show damaged mangroves from 61,700.20 ha in 2010 to 21,944 ha in 2017; this means that 35.6% (39,756.2 ha lost) of the mangrove forest in East Java was damaged within seven years.

3.4. Impact

Illegal logging impacted the loss of mangrove species. The density of mangroves will decrease. It affected the carbon stock and storage loss. Mangrove ecosystems have an essential role as absorbers (sinks) of CO2 from the air. Thus, mangrove forests have an essential role in stabilizing the climate, currently experiencing changes [43]. Carbon stock in Indo-Pacific is about 1023 MgC ha−1 [42] while in Indonesia is 1083 ± 378 MgC ha−1 [77]. Mangrove in Indonesia has an average carbon stock of about 3.14 PgC [77].
Usmawati [76] demonstrated mangroves at the Clungup Beach absorb carbon biomass of 125.87 tons ha−1, and a litter of mangrove leaves can absorb 15.17 kg ha−1 per month. Carbon in a mangrove leaf litter absorbs 0.25 tons ha−1 per month, while the carbon stock in the mangrove stem holds 50.71 tons ha−1. Research results of Fikri [78] on the natural mangrove forest of the Lamongan Regency indicated that the carbon stock was 40.66 MgC ha−1. Rizky [79] showed that the estimated carbon stock in mangrove vegetation in Alas Purwo National Park in Banyuwangi Regency was 2711 tons ha−1, with an average carbon stock of 157.5 kg per tree. Research by Aldus [80] on carbon stock and carbon dioxide absorption in Penunggul Village, Pasuruan Regency, stated that the total estimated amount was 501.99 MgC ha−1 with a carbon dioxide absorption 1840.63 MgC ha−1. Also, Research by Adam [81] on the Coast of Lamongan Regency identified 251,307 tons ha−1. These results are different from Rizky [79], which shows that the estimated total carbon stock in the coastal area of the Lamongan Regency is 181.3 tons C ha−1, and the total absorption is 374.1 tons C ha−1. Based on measurements conducted by Kauffman, et al. [82], measurements in Kalimantan carbon stock are 1259 MgC ha−1. Based on this description, the average carbon sequestration and storage in East Java show great potential. However, the low carbon sequestration and storage in mangrove forests are mainly due to illegal logging, causing deforestation [7,83].

3.5. Response

The first objective is to identify public opinion on irrational use (Figure 4). This figure shows the mangroves’ use that is not compliant with the rule or rational use and unsustainable harvesting. Based on the test using AHP, the highest value is 0.190, i.e., for the alternative “Community does not care about the destruction of mangrove forests.” This opinion is the basis for proposing that illegal logging causes deforestation due to the accessibility of reaching mangrove forests. Accessibility means treating everyone the same concerning the use of mangrove forests and giving them equal opportunities; moreover, the community utilizes for daily living needs. Objective 2 is to learn people’s opinions on illegal logging (Figure 5). The AHP test found that the most popular option was “Mangrove forests are increasingly reduced for residential areas”, with a value of 0.248. Furthermore, forests fire often occur, both intentionally or unintentionally and many areas have also been changed to agriculture, plantations, and animal husbandry. Objective 3 is to identify public opinion on the effect of forest damage on carbon sequestration (Figure 6). The AHP test initiates that the most popular option was “Management of mangrove forests is not optimal”, with a value of 0.206. So, it will have an impact on the weak absorption function of carbon, especially biomass. Objective 4 is to identify public opinion on weak carbon sequestration (Figure 7). The AHP test confirmed the most popular option was “Lack of synergy between the government and the community”, with a value of 0.314, which is also supported by converting mangrove land functions and deforestation intensively. Objective 5 is to identify public opinion on law enforcement (Figure 8). The AHP test demonstrated that the most popular option was “Law enforcement should have limited human resources”, with a value of 0.316, so legal oversight is weak. Especially, there are no clear legal sanctions.
Furthermore, the results of outer model testing were conducted by testing convergent validity (Table 1), discriminant validity, and reliability of each observed research variable (Table 2). A convergent validity test on objective 1, “Irrational use,” obtained four valid indicators from the seven initial indicators used. The valid indicators are: Indicator X1.1, “Community utilizes mangrove forests for daily living needs”, indicator X1.2, “Community does not have awareness about the functions and roles of mangroves”, indicator X1.4, “Community access to mangrove forests is easily achieved”, and indicator X1.6, “There is no supervision from the stakeholders.” These four indicators meet the convergent validity test requirements, which have a factor loading value of more than 0.5.
The convergent validity test on objective 2, namely “Illegal logging”, identified three valid indicators out of the seven initial indicators used. Valid indicators are: Indicator X2.1, “People steal mangrove wood as it has a high sale value”, indicator X2.3, “Declining mangrove forests are used for agriculture, plantations, and animal husbandry”, and indicator X2.7, “The area of mangrove forests is increasingly reduced to be used for aquaculture.” These three indicators meet the convergent validity test requirements, which have a factor loading value of more than 0.5. The convergent validity test on objective 3, namely “Effect of forest damage on carbon sequestration”, obtained four valid indicators from the seven initial indicators used. The valid indicators are: Indicator X3.2, “Absorption of mangrove forests has weakened”, indicator X3.4, “Above-ground biomass (stems, branches, twigs, leaves, flowers, and fruit) absorption is weak”, indicator X3.6, “The tree diameter is getting smaller due to the storage of biomass from the conversion of carbon dioxide (CO2), which is getting smaller in line with the less CO2 absorbed by the mangrove tree”, and indicator X3.7, “Mangrove conservation efforts are not optimal.” These four indicators meet the convergent validity test requirements, which have a factor loading value of more than 0.5.
The convergent validity test on objective 4, namely “Weak carbon sequestration”, identified three valid indicators out of the five initial indicators used. Valid indicators are: Indicator X4.2, “The conversion of mangrove land functions intensively”, indicator X4.4, “Mangrove deforestation is intensive”, and indicator X4.5, “Many mangrove trees are made into charcoal by residents.” These three indicators meet the convergent validity test requirements, which have a factor loading value of more than 0.5.
The convergent validity test results on objective 5, namely “Law enforcement”, obtained three valid indicators from the four initial indicators used. Valid indicators are: Indicator X5.2 “There are no clear legal sanctions”, indicator X5.3 “Low community involvement”, and indicator X5.4 “Limited number of human resources for law enforcement.” These three indicators meet the convergent validity test requirements, which have a factor loading value of more than 0.5. AVE roots value from each latent variable or the destination variable is higher than the correlation between latent variables to meet the discriminant validity test requirements (Table 2). The construct reliability test obtained the Composite Reliability value of each latent variable, which is more than 0.70, and the Cronbach Alpha value of each latent variable is more than 0.60, so it meets the construct reliability requirements.
Testing the inner model was done by testing the influence between latent variables (Table 3). The results of the inner model and hypothesis testing are presented as follows:
  • The influence of variable X1, “Irrational use”, on the X2 variable, “Illegal logging”, obtained a path coefficient of 0.963 with a significance value of 0.000 (p < 0.05). So, a significant positive effect was obtained. It means that the higher the respondent’s perception of variable X1, namely “Irrational use” of mangrove forests, will significantly influence the respondent’s perception of variable X2, namely “Illegal logging”;
  • The influence of the X2 variable, “Illegal logging”, on the X3 variable, “Effect of forest damage on carbon sequestration”, obtained a path coefficient of 0.986 with a significance value of 0.000 (p < 0.05). So, a significant positive effect was obtained. It means that the higher the respondent’s perception of variable X2, namely “Illegal logging”, will significantly influence the respondents’ perceptions of variable X3, namely “effect of forest damage on carbon sequestration”;
  • The influence of variable X2, “Illegal logging”, on variable X5, “Law enforcement”, obtained a path coefficient of 0.867 with a significance value of 0.000 (p < 0.05). So, a significant positive effect was obtained. It means that the higher the respondent’s perception of the X2 variable, namely “Illegal logging”, will significantly influence the respondent’s perception of the variable X5 “Law enforcement”;
  • The influence of the X3 variable, “Effect of forest damage on carbon sequestration”, on the X4 variable, “Weak carbon sequestration”, obtained a path coefficient of 0.927 with a significance value of 0.000 (p < 0.05). So, a significant positive effect was obtained. It means that the higher the respondent is on the X3 variable, namely “Effect of forest damage on carbon sequestration,” will significantly affect the respondent’s perception of the X4, namely “Weak carbon sequestration”.
The constructed model was generating an essential point that irrational use means illegal logging (Figure 9). The respondents’ perception indicated that illegal logging creates irrational activities with no permission, and sometimes the people can exploit mangroves every time they have an opportunity without government supervision. The respondents also mentioned that illegal logging leads to forest damage, which works against carbon sequestration. Data from mangroves’ carbon sequestration and stock show that people will suffer from global warming, and there will be no protection from tsunamis. The presence of illegal logging suggests that law enforcement is weak, and forest damage will lead to weak carbon sequestration. Irrational use ‘will significantly affect the higher respondents’ perceptions of the X2 variable “Illegal logging”. Figure 9 shows the relationship between the five objectives and the relevance of each variable.

4. Discussion

Generally, population growth-impacted human activities increased and land-use changes. Human activities are the main factor in the mangrove loss because of deforestation and anthropogenic pollution [16], including related activities to land use for livelihood like industrial, residential, and aquaculture [46]. This study identified the driver, pressure, state, impact, and responses management of mangrove damage in East Java Province and sampling locations.
The recalculation of land-use data in Indonesia, especially East Java Province, showed the mangrove area decreasing from 2008–2019 (Figure 3a). Otherwise, the population area increased significantly from 2012–2019 (Figure 3c). The fluctuate enhancement occurred in aquaculture in 2014 and increased significantly from 2014–2019 (Figure 3b). The decline in pond opening occurred from 2008–2014 due to the persistent shrimp disease. So, many ponds were closed and planted with mangroves through the rehabilitation program. Re-clearing pond land was occurred until 2019 due to new technology and new types of cultivation commodities. The increasing population area was reinforced by population growth in all regencies and coastal regencies in East Java Provinces in 2008–2019 (Figure 3d,e). The case in Sidoarjo Regency is different because the changes in the mangrove area were fluctuating, which declined from 1995–2009 and increased in 2015 [84]. Damage to the mangrove ecosystem in Sidoarjo is caused by illegal logging activities, converting mangrove land to ponds and housing. The community has still used the mangrove illegally for their livelihoods like firewood, material building, and charcoal. The quality of firewood and charcoal from mangroves is the temperate stable and well strength for pole, house, bridge, and port. Firm law enforcement has not been carried out optimally. Increasing the mangrove area occurred due to the mangrove conservation and rehabilitation program. In different cases, the government regulation of the ecotourism program can improve the area and density of mangrove [49], such as in the Pasuruan Regency [85] and Lamongan Regency [86] because it is supported by the mangroves conservation and rehabilitation program.
Mangrove has an ecological function, i.e., sediment trap, wave barrier, carbon storage and carbon sequestration [87]. The impact of illegal logging is related to the loss of density, so it lost carbon sequestration and sink function. Carbon sequestration decreased in the atmosphere while the source of carbon still rises because of the industry. Sejati, Buchori, Kurniawati, Brana and Fariha [47] showed a carbon storage loss of about 20% from 2015–2019 because of industry activities, port, and reclamation. Slamet, Dargusch, Aziz and Wadley [48] reported that reclamation could influence the carbon storage and stock in sediment and mangrove ecosystem.
Based on AHP and PLS models, low community awareness, worst forest management, weak law enforcement and lack of synergy between government and community were identified as drivers of the mangrove damage. All factors were affected by human activities for their livelihoods and showed the community did not care about the mangrove damage. In fact, community awareness efforts need to be carried out, both through capacity building programs, environmental campaigns, and similar programs, as well as active community involvement in any environmental conservation programs. Additionally, the management response was made following the mandate of Law Number 32 of 2009; it is necessary to protect and manage the environment to realize a healthy living environment. A strategy to prevent illegal logging and increase carbon sequestration and storage efforts is to be carried out as follows:
(1)
The government has the responsibility to guarantee citizens’ rights to a healthy environment. The government must prevent the illegal logging and all other destructive activities of mangroves in the coastal environment;
(2)
Utilization and management of natural resources must adhere to sustainable development. Thus, every citizen has the responsibility to preserve mangrove forests, including those responsible for future generations;
(3)
Utilization and management techniques for mangrove forests must pay attention to various aspects, including economic, socio-cultural, and bio-geophysical characteristics [45];
(4)
The principle of polluting pay must be applied. That means that every person in charge of a business or individuals who cause pollution or damage to mangrove forests must pay a fine;
(5)
Every community member must participate in decision-making processes and the protection and management of mangrove forests. Replant needed to increase the density of mangrove and maintain a stable carbon stock and storage. The ecotourism concept is used to protect and manage the mangrove density, species, and areas.

5. Conclusions

The damage and loss of mangrove area have been quite massive in East Java Province in recent years. This situation causes the loss of the ability of the mangrove ecosystem to absorb and store carbon. The driver factor was population growth and new aquaculture (shrimp and salt pan), so the pressure was land-use change and illegal logging. On the other hand, many people depend on their livelihood by utilizing the mangrove ecosystem. Stop the illegal logging activities and monitoring and evaluating the land-use change can be a management response to protect and save mangroves. The mitigation of impact and recondition of the mangrove ecosystem must be carried out consistently. Increasing efforts to utilize mangroves based on ecosystem services is a necessity. Public awareness and law enforcement efforts need to be carried out simultaneously. Additionally, program synchronization inter-sectors must be achieved to be mutually supportive and sustainable, both related to economic development and conservation.

Author Contributions

Conceptualization, R.R. and D.G.B.; methodology, R.R. and D.G.B.; software, R.R.; validation, R.R. and D.G.B.; formal analysis, R.R., D.G.B. and F.K.; investigation, R.R.; resources, R.R.; data curation, R.R.; writing—original draft preparation, R.R. and D.G.B.; writing—review and editing, D.G.B. and F.K.; visualization, D.G.B. and F.K; supervision, D.G.B. and F.K.; project administration, R.R.; funding acquisition, R.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We appreciate the support from the rectors of both Brawijaya University and the IPB University, who allowed us to carry out collaborative research in research locations in East Java Province. We also appreciate our colleagues who took their time to review this article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Pendleton, L.; Donato, D.C.; Murray, B.C.; Crooks, S.; Jenkins, W.A.; Sifleet, S.; Craft, C.; Fourqurean, J.W.; Kauffman, J.B.; Marba, N.; et al. Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS ONE 2012, 7, e43542. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Chen, G.; Chen, B.; Yu, D.; Tam, N.F.Y.; Ye, Y.; Chen, S. Soil greenhouse gas emissions reduce the contribution of mangrove plants to the atmospheric cooling effect. Environ. Res. Lett. 2016, 11, 124019. [Google Scholar] [CrossRef]
  3. Kumara, M.P.; Jayatissa, L.P.; Krauss, K.W.; Phillips, D.H.; Huxham, M. High mangrove density enhances surface accretion, surface elevation change, and tree survival in coastal areas susceptible to sea-level rise. Oecologia 2010, 164, 545–553. [Google Scholar] [CrossRef] [PubMed]
  4. Krauss, K.W.; McKee, K.L.; Lovelock, C.E.; Cahoon, D.R.; Saintilan, N.; Reef, R.; Chen, L. How mangrove forests adjust to rising sea level. New Phytol. 2014, 202, 19–34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Kennedy, H.; Beggins, J.; Duarte, C.M.; Fourqurean, J.W.; Holmer, M.; Marbà, N.; Middelburg, J.J. Seagrass sediments as a global carbon sink: Isotopic constraints. Glob. Biogeochem. Cycles 2010, 24. [Google Scholar] [CrossRef] [Green Version]
  6. Sasmito, S.D.; Taillardat, P.; Clendenning, J.N.; Cameron, C.; Friess, D.A.; Murdiyarso, D.; Hutley, L.B. Effect of land-use and land-cover change on mangrove blue carbon: A systematic review. Glob. Chang. Biol. 2019, 25, 4291–4302. [Google Scholar] [CrossRef]
  7. Rudianto. Restoration of mangrove ecosystem in coastal village based on co-management. J. Perikan. Univ. Gadjah Mada 2018, 20, 1–12. [Google Scholar] [CrossRef] [Green Version]
  8. Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J.; Duke, N. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 2011, 20, 154–159. [Google Scholar] [CrossRef]
  9. Giesen, W.; Wulffraat, S.; Zieren, M.; Scholten, L. Mangrove Guidebook for Southeast Asia; FAO Regional Office for Asia and the Pacific: Bangkok, Thailand, 2007. [Google Scholar]
  10. Spalding, M.; Kainuma, M.; Collins, L. World Atlas of Mangroves; Routledge: Abingdon, UK, 2010. [Google Scholar]
  11. Rudianto, R.; Bengen, D.G. Identification of the types of damage to mangrove forest ecosystems due to human activities in Lowu-Lowu Village, Lea-Lea District, Baubau City. J. Geogr. Educ. Res. 2016, 1, 30–45. [Google Scholar] [CrossRef]
  12. Richards, D.R.; Friess, D.A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. USA 2016, 113, 344–349. [Google Scholar] [CrossRef] [Green Version]
  13. Bunting, P.; Rosenqvist, A.; Lucas, R.; Rebelo, L.-M.; Hilarides, L.; Thomas, N.; Hardy, A.; Itoh, T.; Shimada, M.; Finlayson, C. The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent. Remote Sens. 2018, 10, 1669. [Google Scholar] [CrossRef] [Green Version]
  14. Batubara, R.R.; Jogia, G.E.; Batubara, I.; Audah, K.A.; Nunuk, K.N. Introduction of bioprospecting opportunities for Indonesian mangrove species. IOP Conf. Ser. Earth Environ. Sci. 2018, 183. [Google Scholar] [CrossRef]
  15. Rahadian, A.; Prasetyo, L.B.; Setiawan, Y.; Wikantika, K. A Historical review of data and information of Indonesian mangroves area. Media Konserv. 2019, 24, 163–178. [Google Scholar] [CrossRef] [Green Version]
  16. Ilman, M.; Dargusch, P.; Dart, P.; Onrizal, A. historical analysis of the drivers of loss and degradation of Indonesia’s mangroves. Land Use Policy 2016, 54, 448–459. [Google Scholar] [CrossRef]
  17. Silvius, M.; Steeman, A.; Berczy, E.; Djuharsa, E.; Tanfik, A. The Indonesian wetland inventory. A preliminary compilation of existing information on wetlands of Indonesia; Edwin: Bogor, Indonesia, 1987. [Google Scholar]
  18. Burbridge, P.R. Management of mangrove exploitation in Indonesia. Appl. Geogr. 1982, 2, 39–54. [Google Scholar] [CrossRef]
  19. Soegiarto, A.; Sukardjo, S. Utilization of mangrove forest in brackish water culture pond development in Indonesia. J. Penelit. Dan Pengemb. Pertan. 1987, 6, 95–104. [Google Scholar]
  20. (RePPProT). The Land Resources of Indonesia: A National Overview; Regional Physical Planning Programme for Transmigration: Jakarta, Indonesia, 1990. [Google Scholar]
  21. Naamin, N. Conversion of mangrove areas to tambaks aquaculture in Indonesia. In Proceedings of the Workshop on the conversion of mangrove areas to aquaculture, Iloilo, Visayas, Philiphines, 24–26 April 1986; pp. 56–71. [Google Scholar]
  22. Choong, E.T.; Wirakusumah, R.S.; Achmadi, S.S. Mangrove forest resources in Indonesia. For. Ecol. Manag. 1990, 33, 45–47. [Google Scholar] [CrossRef]
  23. (BIG). Mangrove Geospatial Information of Indonesia; Pusat Pemetaan dan Informasi Tematik; Badan Informasi Geospasial: Bogor, Indonesia, 2012.
  24. (KLHK). Penghitungan Deforestasi Indonesia Periode 2009–2011 (Calculation of Deforestation of Indonesia for the Period of 2009–2011); Ministry of Forestry, Republic of Indonesia: Jakarta, Indonesia, 2012.
  25. Susanto, A.; Subarya, C.; Poniman, A. Kebijakan Satu Peta; Momentum Reformasi Penyelenggaraan Informasi Geospasial Nasional. In Proceedings of the Seminar Nasional Geomatika, Bogor, Indonesia, 5 October 2016; pp. 23–34. [Google Scholar]
  26. Badan Pusat Statistik. East Java Province in Figure 2019; BPS-Statistics of Jawa Timur Province: Jakarta, Indonesia, 2019.
  27. Badan Pusat Statistik. East Java Province in Figure 2008; BPS-Statistics of Jawa Timur Province: Jakarta, Indonesia, 2008.
  28. Badan Pusat Statistik. East Java Province in Figure 2012; BPS-Statistics of Jawa Timur Province: Jakarta, Indonesia, 2012.
  29. Badan Pusat Statistik. East Java Province in Figure 2014; BPS-Statistics of Jawa Timur Province: Jakarta, Indonesia, 2014.
  30. Badan Pusat Statistik. East Java Province in Figure 2016; BPS-Statistics of Jawa Timur Province: Jakarta, Indonesia, 2016.
  31. Ministry of Environment and Forestry. Rekalkulasi Penutupan Lahan Indonesia Tahun 2008 (Land Cover Recalculation of Indonesia Year 2008); Directorate of Inventory and Monitoring of Forest Resources, Directorate General of Forestry Planning and Environmental Management, Ministry of Environment and Forestry: Jakarta, Indonesia, 2008.
  32. Ministry of Environment and Forestry. Rekalkulasi Penutupan Lahan Indonesia Tahun 2012 (Land Cover Recalculation of Indonesia Year 2012); Directorate of Inventory and Monitoring of Forest Resources, Directorate General of Forestry Planning and Environmental Management, Ministry of Environment and Forestry: Jakarta, Indonesia, 2012.
  33. Ministry of Environment and Forestry. Rekalkulasi Penutupan Lahan Indonesia Tahun 2014 (Land Cover Recalculation of Indonesia Year 2014); Directorate of Inventory and Monitoring of Forest Resources, Directorate General of Forestry Planning and Environmental Management, Ministry of Environment and Forestry: Jakarta, Indonesia, 2014.
  34. Ministry of Environment and Forestry. Rekalkulasi Penutupan Lahan Indonesia Tahun 2016 (Land Cover Recalculation of Indonesia Year 2016); Directorate of Inventory and Monitoring of Forest Resources, Directorate General of Forestry Planning and Environmental Management, Ministry of Environment and Forestry: Jakarta, Indonesia, 2016.
  35. Ministry of Environment and Forestry. Rekalkulasi Penutupan Lahan Indonesia Tahun 2019 (Land Cover Recalculation of Indonesia Year 2019); Directorate of Inventory and Monitoring of Forest Resources, Directorate General of Forestry Planning and Environmental Management, Ministry of Environment and Forestry: Jakarta, Indonesia, 2019.
  36. Nobre, A.M. Scientific approaches to address challenges in coastal management. Mar. Ecol. Prog. Ser. 2011, 434, 279–289. [Google Scholar] [CrossRef] [Green Version]
  37. Sarmin, N.S.; Mohd Hasmadi, I.; Pakhriazad, H.Z.; Khairil, W.A. The DPSIR framework for causes analysis of mangrove deforestation in Johor, Malaysia. Environ. Nanotechnol. Monit. Manag. 2016, 6, 214–218. [Google Scholar] [CrossRef]
  38. Adams, J.B.; Rajkaran, A. Changes in mangroves at their southernmost African distribution limit. Estuar. Coast. Shelf Sci. 2020. [Google Scholar] [CrossRef]
  39. Partovi Fariborz, Y.; Burton, J.; Banerjee, A. Application of Analytical Hierarchy Process in Operations Management. Int. J. Oper. Prod. Manag. 1990, 10, 5–19. [Google Scholar] [CrossRef]
  40. Barker, M.; Rayens, W. Partial least squares for discrimination. J. Chemom. 2003, 17, 166–173. [Google Scholar] [CrossRef]
  41. Budianto, A.; Kusdarini, E.; Effendi, S.S.W.; Aziz, M. The Production of Activated Carbon from Indonesian Mangrove Charcoal. IOP Conf. Ser. Mater. Sci. Eng. 2019, 462. [Google Scholar] [CrossRef]
  42. Donato, D.C.; Kauffman, J.B.; Murdiyarso, D.; Kurnianto, S.; Stidham, M.; Kanninen, M. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 2011, 4, 293–297. [Google Scholar] [CrossRef]
  43. Heriyanto, N.; Subiandono, E. Composition and structure of stands, biomass, and potential carbon content of mangrove forests in Alas Purwo National Park. J. For. Res. Nat. Conserv. 2012, 9, 23–32. [Google Scholar]
  44. Kridiborworn, P.; Chidthaisong, A.; Yuttitham, M.; Tripetchkul, S. Carbon sequestration by mangrove forest planted specifically for charcoal production in Yeesarn, Samut Songkram. J. Sustain. Energy Environ. 2012, 3, 87–92. [Google Scholar]
  45. Murray, B.C.; Jenkins, W.A.; Sifleet, S.; Pendleton, L.; Baldera, A. Payments for Blue Carbon: Potential for Protecting Threatened Coastal Habitats; Nicholas Institute for Environmental Policy Solutions, Duke University: Durham, NC, USA, 2010. [Google Scholar]
  46. Primavera, J.H. Mangrove, fishponds, and the quest for sustainability. Science 2005, 310, 57–59. [Google Scholar] [CrossRef] [Green Version]
  47. Sejati, A.W.; Buchori, I.; Kurniawati, S.; Brana, Y.C.; Fariha, T.I. Quantifying the impact of industrialization on blue carbon storage in the coastal area of Metropolitan Semarang, Indonesia. Appl. Geogr. 2020, 124. [Google Scholar] [CrossRef]
  48. Slamet, N.S.; Dargusch, P.; Aziz, A.A.; Wadley, D. Mangrove vulnerability and potential carbon stock loss from land reclamation in Jakarta Bay, Indonesia. Ocean Coast. Manag. 2020, 195. [Google Scholar] [CrossRef]
  49. Turschwell, M.P.; Tulloch, V.J.D.; Sievers, M.; Pearson, R.M.; Andradi-Brown, D.A.; Ahmadia, G.N.; Connolly, R.M.; Bryan-Brown, D.; Lopez-Marcano, S.; Adame, M.F.; et al. Multi-scale estimation of the effects of pressures and drivers on mangrove forest loss globally. Biol. Conserv. 2020, 247. [Google Scholar] [CrossRef]
  50. Yang, S.; Hu, S.; Wang, S.; Zou, L. Effects of rapid urban land expansion on the spatial direction of residential land prices: Evidence from Wuhan, China. Habitat Int. 2020, 101. [Google Scholar] [CrossRef]
  51. Yatim, B.B.; Hoi, W.K. The quality of charcoal from various types of wood. Fuel 1987, 66, 1305. [Google Scholar] [CrossRef]
  52. Sulaiman, B.; Bambang, A.N.; Purnaweni, H.; Lutfi, M.; Mohammed, E.M.A. Coastal community perception of mangrove in Suli Subdistrict, Luwu. J. Pendidik. IPA Indones. 2019, 8, 561–569. [Google Scholar] [CrossRef]
  53. Aksornkoae, S. Mangroves … Coastal treasure of Thailand. J. R. Inst. Thail. 2012, 4, 59–77. [Google Scholar]
  54. Kustanti, A.; Nugroho, B.; Darusman, D.; Kusmana, C. Integrated managemeng of mangroves ecosystem in Lampung Mangrove Center (LMC) East Lampung Regency, Indonesia. J. Coast. Dev. 2012, 15, 209–216. [Google Scholar]
  55. Alemu, I.J.B.; Richards, D.R.; Gaw, L.Y.-F.; Masoudi, M.; Nathan, Y.; Friess, D.A. Identifying spatial patterns and interactions among multiple ecosystem services in an urban mangrove landscape. Ecol. Indic. 2021, 121, 107042. [Google Scholar] [CrossRef]
  56. Arumugam, M.; Niyomugabo, R.; Dahdouh-Guebas, F.; Hugé, J. The perceptions of stakeholders on current management of mangroves in the Sine-Saloum Delta, Senegal. Estuar. Coast. Shelf Sci. 2020. [Google Scholar] [CrossRef]
  57. Beymer-Farris, B.A.; Bassett, T.J. The REDD menace: Resurgent protectionism in Tanzania’s mangrove forests. Glob. Environ. Chang. 2012, 22, 332–341. [Google Scholar] [CrossRef]
  58. Dahdouh-Guebas, F.; Hugé, J.; Abuchahla, G.M.O.; Cannicci, S.; Jayatissa, L.P.; Kairo, J.G.; Arachchilage, S.K.; Koedam, N.; Mafaziya Nijamdeen, T.W.G.F.; Mukherjee, N.; et al. Reconciling nature, people and policy in the mangrove social-ecological system through the adaptive cycle heuristic. Estuar. Coast. Shelf Sci. 2020. [Google Scholar] [CrossRef]
  59. Glaser, M.; Krause, G.; Oliveira, R.S.; Fontalvo-Herazo, M. Mangroves and People: A Social-Ecological System. In Mangrove Dynamics and Management in North Brazil; Saint-Paul, U., Schneider, H., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 307–351. [Google Scholar]
  60. Moschetto, F.A.; Ribeiro, R.B.; De Freitas, D.M. Urban expansion, regeneration and socioenvironmental vulnerability in a mangrove ecosystem at the southeast coastal of São Paulo, Brazil. Ocean Coast. Manag. 2020. [Google Scholar] [CrossRef]
  61. Onyena, A.P.; Sam, K. A review of the threat of oil exploitation to mangrove ecosystem: Insights from Niger Delta, Nigeria. Glob. Ecol. Conserv. 2020, 22, e00916. [Google Scholar] [CrossRef]
  62. Ounvichit, T.; Yoddumnern-Attig, B. Community dialogs on the probabilities of community-based mangrove institution. Kasetsart J. Soc. Sci. 2018, 39, 365–373. [Google Scholar] [CrossRef]
  63. Rasquinha, D.N.; Mishra, D.R. Impact of wood harvesting on mangrove forest structure, composition and biomass dynamics in India. Estuar. Coast. Shelf Sci. 2020, 106974. [Google Scholar] [CrossRef]
  64. Sharma, S.; MacKenzie, R.A.; Tieng, T.; Soben, K.; Tulyasuwan, N.; Resanond, A.; Blate, G.; Litton, C.M. The impacts of degradation, deforestation and restoration on mangrove ecosystem carbon stocks across Cambodia. Sci. Total Environ. 2020, 706, 135416. [Google Scholar] [CrossRef] [PubMed]
  65. Triyanti, A.; Bavinck, M.; Gupta, J.; Marfai, M.A. Social capital, interactive governance and coastal protection: The effectiveness of mangrove ecosystem-based strategies in promoting inclusive development in Demak, Indonesia. Ocean Coast. Manag. 2017, 150, 3–11. [Google Scholar] [CrossRef]
  66. Veettil, B.K.; Ward, R.D.; Quang, N.X.; Trang, N.T.T.; Giang, T.H. Mangroves of Vietnam: Historical development, current state of research and future threats. Estuar. Coast. Shelf Sci. 2019, 218, 212–236. [Google Scholar] [CrossRef]
  67. Yu, C.; Feng, J.; Liu, K.; Wang, G.; Zhu, Y.; Chen, H.; Guan, D. Changes of ecosystem carbon stock following the plantation of exotic mangrove Sonneratia apetala in Qi’ao Island, China. Sci. Total Environ. 2020, 717, 137142. [Google Scholar] [CrossRef]
  68. He, Q.; Silliman, B.R. Climate Change, Human Impacts, and Coastal Ecosystems in the Anthropocene. Curr. Biol. 2019, 29, R1021–R1035. [Google Scholar] [CrossRef]
  69. Giesen, W. Indonesia’s mangroves: An update on remaining area and main management issues. In Proceedings of the International Seminar on Coastal Zone Management of Small Island Ecosystems, Ambon, Indonesia, 7–10 April 1993. [Google Scholar]
  70. Bu, H.; Meng, W.; Zhang, Y.; Wan, J. Relationships between land use patterns and water quality in the Taizi River basin, China. Ecol. Indic. 2014, 41, 187–197. [Google Scholar] [CrossRef]
  71. Park, Y.-S.; Kwon, Y.-S.; Hwang, S.-J.; Park, S. Characterizing effects of landscape and morphometric factors on water quality of reservoirs using a self-organizing map. Environ. Model. Softw. 2014, 55, 214–221. [Google Scholar] [CrossRef]
  72. Huang, C.; Zhang, M.; Zou, J.; Zhu, A.X.; Chen, X.; Mi, Y.; Wang, Y.; Yang, H.; Li, Y. Changes in land use, climate and the environment during a period of rapid economic development in Jiangsu Province, China. Sci. Total Environ. 2015, 536, 173–181. [Google Scholar] [CrossRef] [PubMed]
  73. Michael, E. Analisis Perubahan Hutan Mangrove Menggunakan Citra Landsat 8 (Studi Kasus: Kabupaten Pasuruan dan Kota Probolinggo); Institut Teknologi Nasional: Malang, Indonesia, 2019. [Google Scholar]
  74. Nurcahyani, N.F.; Mustafa, L.D.; Elfa, M.P. Analisa perubahan luas dan kerapatan mangrove melalui pengolahan citra satelit Landsat 8. J. JARTEL 2019, 9, 17. [Google Scholar]
  75. Hidayah, Z.; Wiyanto, D.B. Analisa temporal perubahan luas hutan mangrove di Kabupaten Sidoarjo dengan memanfaatkan data citra satelit. Junal Bumi Lestari 2013, 13, 318–332. [Google Scholar]
  76. Usmawati, M. Estimation of Carbon Biomass in Mangrove Stem and Leaf Litter at Clungup Beach, Tambakrejo Village, Sumbermanjing Wetan District, Malang Regency. Bachelor’s Thesis, Brawijaya University, Malang, Indonesia, 2015. [Google Scholar]
  77. Murdiyarso, D.; Purbopuspito, J.; Kauffman, J.B.; Warren, M.W.; Sasmito, S.D.; Donato, D.C.; Manuri, S.; Krisnawati, H.; Taberima, S.; Kurnianto, S. The potential of Indonesian mangrove forests for global climate change mitigation. Nat. Clim. Chang. 2015, 5, 1089–1092. [Google Scholar] [CrossRef]
  78. Fikri, M.Z. Comparative Analysis of Carbon and Nitrogen Stocks in Sediments in Natural and Artificial Mangrove Forests in Lamongan Regency. Bachelor’s Thesis, Brawijaya University, Malang, Indonesia, 2017. [Google Scholar]
  79. Rizky, P.N.H. Estimation of Biomass and Carbon Stock in Live Mangrove Vegetation (Live Trees) and Surface Sediment: A Case Study of Mangrove Forest in the Bedul Resort Block, Grajagan Segoro Anak, Alas Purwo National Park (TNAP) Banyuwangi Regency, East Java Province. Bachelor’s Thesis, Brawijaya University, Malang, Indonesia, 2014. [Google Scholar]
  80. Aldus, W. Study of Carbon Stock and Carbon Dioxide Absorption in Mangrove Ecosystems in Penunggul Village, Pasuruan Regency. Bachelor’s Thesis, Brawijaya University, Malang, Indonesia, 2017. [Google Scholar]
  81. Adam, B.R. Estimation of Above and Below Ground Carbon Stocks and Mangrove CO2 Uptake Capacity in the Coastal Areas of Lamongan Regency. Bachelor’s Thesis, Brawijaya University, Malang, Indonesia, 2016. [Google Scholar]
  82. Kauffman, J.; Arifanti, V.; Basuki, I.; Kurnianto, S.; Novita, N.; Murdiyarso, D.; Donato, D.C.; Warren, M.W. Protocols for the Measurement, Monitoring, and Reporting of Structure, Biomass, Carbon Stocks and Greenhouse Gas Emissions in Tropical Peat Swamp Forests; Working Paper; CIFOR: Bogor, Indonesia, 2016. [Google Scholar]
  83. Nabuurs, G.J.; Masera, O.; Andrasko, K.; Benitez-Ponce, P.; Boer, R.; Dutschke, M.; Elsiddig, E.; Ford-Robertson, J.; Frumhoff, P.; Karjalainen, T.; et al. Forestry. Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Metz, B., Davidson, O., Bosch, P., Dave, R., Meyer, L., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007. [Google Scholar]
  84. Maryantika, N.; Lin, C. Exploring changes of land use and mangrove distribution in the economic area of Sidoarjo District, East Java using multi-temporal Landsat images. Inf. Process. Agric. 2017, 4, 321–332. [Google Scholar] [CrossRef]
  85. Hidayah, K.B.R.A.; Perdanawati, R.A.; Junaidi, R. Evaluasi perubahan penggunaan lahan pesisir di Kota Pasuruan. In Proceedings of the Prosiding Seminar Nasional Kelautan dan Perikanan IV 2018, Surabaya, Indonesia, 5 September 2018. [Google Scholar]
  86. Hapsari, W.F. Pemetaan Dinamika Mangrove di Kabupaten Lamongan dengan Data Inderaja. Bachelor’s Thesis, Airlangga University, Surabaya, Indonesia, 2019. [Google Scholar]
  87. Giri, C.; Pengra, B.; Zhu, Z.; Singh, A.; Tieszen, L.L. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci. 2007, 73, 91–100. [Google Scholar] [CrossRef]
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Figure 1. Location of the research area (CLR = Coastal of Lamongan Regency; PVP = Penunggal Village, Pasuruan Regency; APP = Alas Purwo National Park; and CCM = Clungup Coastal, Malang Regency).
Figure 1. Location of the research area (CLR = Coastal of Lamongan Regency; PVP = Penunggal Village, Pasuruan Regency; APP = Alas Purwo National Park; and CCM = Clungup Coastal, Malang Regency).
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Figure 2. Status of mangrove ecosystem in East Java Province, Indonesia using DPSIR framework.
Figure 2. Status of mangrove ecosystem in East Java Province, Indonesia using DPSIR framework.
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Figure 3. Mangrove area in East Java Province and the driver of deforestation. (a) The mangrove deforestation from 1991–2019, (b) the changes of aquaculture area from 1991-2019, (c) the changes of the residential area from 2008–2019, (d) the population growth from 2008–2019, (e) the population growth in coastal regencies from 2008–2019, and (f) the timber production of logs from 2014–2019.
Figure 3. Mangrove area in East Java Province and the driver of deforestation. (a) The mangrove deforestation from 1991–2019, (b) the changes of aquaculture area from 1991-2019, (c) the changes of the residential area from 2008–2019, (d) the population growth from 2008–2019, (e) the population growth in coastal regencies from 2008–2019, and (f) the timber production of logs from 2014–2019.
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Figure 4. Irrational use with its variables.
Figure 4. Irrational use with its variables.
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Figure 5. Illegal logging with its variables.
Figure 5. Illegal logging with its variables.
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Figure 6. Effect of forest damage on carbon sequestration and its variables.
Figure 6. Effect of forest damage on carbon sequestration and its variables.
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Figure 7. Weak carbon sequestration and its variables.
Figure 7. Weak carbon sequestration and its variables.
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Figure 8. Law enforcement and its variables.
Figure 8. Law enforcement and its variables.
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Figure 9. Structural model.
Figure 9. Structural model.
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Table
Table 1. Convergent validity results.
Table 1. Convergent validity results.
Original Sample (O)Standard Error (STERR)T Statistics (O/STERR)P-valueRemarks
X1.1 ← X10.8180.01846.4570.000Valid
X1.2 ← X10.5710.03018.8680.000Valid
X1.4 ← X10.8410.008100.7950.000Valid
X1.6 ← X10.7630.01842.1540.000Valid
X1.7 ← X10.8180.01846.4570.000Valid
X2.1 ← X20.6810.03022.5780.000Valid
X2.3 ← X20.9450.004241.5980.000Valid
X2.7 ← X20.6770.02626.0790.000Valid
X3.2 ← X30.9450.003372.0050.000Valid
X3.4 ← X30.9450.003372.0050.000Valid
X3.6 ← X30.5830.02919.8840.000Valid
X3.7 ← X30.7750.02236.0180.000Valid
X4.2 ← X40.9800.0011062.2470.000Valid
X4.4 ← X40.9800.0011062.2470.000Valid
X4.5 ← X40.7280.03222.7700.000Valid
X5.2 ← X50.9300.006154.8080.000Valid
X5.3 ← X50.6880.02230.9440.000Valid
X5.4 ← X50.6860.02231.3760.000Valid
Table
Table 2. The result of discriminant validity and constructive reliability.
Table 2. The result of discriminant validity and constructive reliability.
Discriminant ValidityConstructive Reliability
AVE RootsX1X2X3X4X5Composite ReliabilityCronbach’s Alpha
X10.7691.0000.5640.6130.6050.6380.8770.822
X20.7780.5631.0000.6860.6590.6670.8170.652
X30.8260.6130.6861.0000.6270.6340.8920.839
X40.9040.6050.6590.6271.0000.6180.9290.884
X50.7770.6380.6670.6340.6181.0000.8170.652
Table
Table 3. Inner model results and hypothesis testing.
Table 3. Inner model results and hypothesis testing.
Original Sample (O)Standard Error (STERR)T Statistics (O/STERR)P-valueRemarks
X1 → X20.9630.003347.9600.000Significant
X2 → X30.9860.0011272.1640.000Significant
X2 → X50.8670.01270.9590.000Significant
X3 → X40.9270.002424.3620.000Significant
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Rudianto, R.; Bengen, D.G.; Kurniawan, F. Causes and Effects of Mangrove Ecosystem Damage on Carbon Stocks and Absorption in East Java, Indonesia. Sustainability 2020, 12, 10319. https://0-doi-org.brum.beds.ac.uk/10.3390/su122410319

AMA Style

Rudianto R, Bengen DG, Kurniawan F. Causes and Effects of Mangrove Ecosystem Damage on Carbon Stocks and Absorption in East Java, Indonesia. Sustainability. 2020; 12(24):10319. https://0-doi-org.brum.beds.ac.uk/10.3390/su122410319

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Rudianto, Rudianto, Dietriech G. Bengen, and Fery Kurniawan. 2020. "Causes and Effects of Mangrove Ecosystem Damage on Carbon Stocks and Absorption in East Java, Indonesia" Sustainability 12, no. 24: 10319. https://0-doi-org.brum.beds.ac.uk/10.3390/su122410319

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