Assessment of the mangrove protected area in the Eastern Coast of Surabaya
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Assessment of the mangrove protected area in the Eastern Coast of Surabaya
1Viv Djanat Prasita, 2Nuhman*, 3Agus Subianto and 4Agoes Soegianto*
1Department of Oceanography, Faculty of Engineering and Marine Science, University of Hang Tuah, Surabaya, Indonesia
2Department of Fisheries, Faculty of Engineering and Marine Science, University of Hang Tuah, Surabaya, Indonesia
3Department of Public Administration, Faculty of Social and Political Sciences, University of Hang Tuah, Surabaya, Indonesia
4Department of Biology, Faculty of Sciences and Technology, Universitas Airlangga, Surabaya, Indonesia
(Received 19 March, 2019; Accepted 29 May, 2019)
ABSTRACT
This paper aims to assess and evaluate the coastal land in the Mangrove Protected Areas (MPA) of the Eastern Coast of Surabaya (ECS) with sustainable approach. The research focuses on three points, i.e.: to assess the utilization of mangrove conservation zones, to evaluate the coastal land utilization for aquaculture as well as conservation, and to make a model for prediction of the coastal land for future handling. Some methods used in the research, i.e.: GIS method and interview with farmer for assessment the mangrove conservation zone, regression analysis for understanding production trends of the aquaculture and dinamic model for understanding the coastal land area changes. Results indicate that three points are needed seroius attention related to unsustainable resources utilization in the MPA of ECS. First, there are differences between planning and implementation in term of coastal land utilization. Second, land utilization for brackish water ponds is not appropriate because the production of them tend to decrease. Third, the addition of the coastal land tend to increase but it is not to add the areas of MPA of ECS. These findings are unsustainable causes of the coastal land utilization in MPA of ECS. Therefore, they should be attended seriously by local goverment for achieving sustainable development of MPA of ECS.
Key words: Mangrove, Surabaya, Conservation zone, Aquaculture
Introduction
Surabaya is a city as the second largest coastal city in Indonesia. In the National Long Term Develop- ment Plan, Surabaya is designated as the National Development Center of eastern Indonesia so that it is very interesting to relate the planning and design of Surabaya in addressing the current global warm- ing Issues (Respati, 2013). In addressing the prob-
lem of global warming, the role of mangrove pro- tected areas (MPA) in the Eastern Coast of Surabaya (ECS, local name is Pamurbaya) is very important because the area is the only wide and remaining mangrove ecosystem in Surabaya. Moreover, this areas, as habitat for many spesies of mangroves, fish, and many various animals, has an important role in the sustainaibility of the local economy and development of City of Surabaya.
In addition, MPA is an important part of Spatial Planning of Surabaya 2009-2029 because it is green open space of the Surabaya city. Formal regulations in Indonesia bring the proportion of urban green open space in the Law of the Republic of Indonesia on Act No 26 year 2007 on Spatial Planning, which sets a minimum of 30% green city open spaces of the total city area and the proportion of public green open spaces in urban areas shall be at least 20 (twenty) percent of the total area of the city. Accord- ing to Bappeko (2016), percentage of the areas of ECS of the city is 7,48 %, ie. area of ECS are 2,503,9 hectares and areas of the city of Surabaya are 33,451.00 hectares. That is why, mangrove area in ECS must be determined as MPA in order to sup- port sustainable development of the city of Surabaya.
The MPA determination is based on the Surabaya City Regulation No 12 year 2014 (For- merly the regulation number 3 years 2007). The MPA provides protection on a local scale in the sur- rounding area or city scale and serves as a water catchment area, flood prevention, erosion, and to protect ecosystems in the area. Determination of the area aims to preserve the potential and natural re- sources, prevent the occurrence of environmental damage, and avoid various businesses and / or ac- tivities in the land area that can cause environmen- tal damages.
Mangrove ecosystem damage also occurred in the ECS. A review of 2008 is known that the condi- tion of 40% or about 400 acres mangrove forests in the region of ECS is in damaged condition (Respati, 2013). There was a 29.8 km long coastline of man- grove in the region of ECS, now only 8.7 km of man- grove vegetation is overgrown with a thickness of not more than 50 meters. This is very different from the situation in the 1990s, where the thickness of the mangrove forest can be more than 50 meters and they grew up along the shoreline in the ECS. Such conditions make the environmental quality in ECS decline because the less mangrove forest area will be more erosion. According to Olaniyi et al. (2012), mangroves dominating coastal locations exhibit less erosion than areas with non-vegetated land areas.
Another problem related to the area is that some of the MPA land has been owned by the community for a long time. They use the conservation area for aquaculture. According to Parks et al. (1994), con- version of mangrove ecosystems to shrimp ponds may have obtained short-term profit at the expense
of long-term productivity. For the benefit of conser- vation or protection of the area, the local govern- ment must compensate for the loss of the coastal lands. This problem is one of the causes of man- grove damage in the MPA. Local government con- flicts with these communities often occur in pro- tected areas, such as those occurring in Segara Anakan investigated by Dharmawan (2017).
Therefore, the handling of mangroves in ECS is very important so that the ecosystem damage is not getting worse. One useful mangrove function is to serve as a coastal defence system against the tidal storms, tsunamis and ocean waves (Olaniyi, et al., 2012).
From the description above, the issues that need to be elaborated are (1) to what extent the utilization of coastal lands in the MPA ?, (2) how the produc- tivity of communities’ ponds because part of the lands used by the community, (3) how is the trend of coastal land changes in the MPA so that the con- dition can be utilized to strengthen the function of the MPA ?
This paper aims to provide alternative solutions to the above problems by 1). assessing the utiliza- tion of land in the MPA, including the constraints, extent and types of utilization with GIS method/
approach, 2). evaluating pond productivity with lin- ear regression, 3). analyzing the changing trends of land areas using dynamic model that can strengthen the MPA functions. They are the main capitals to support sustainable coastal resource management in the MPA.
Materials and Methods
Study Area
The MPA site covering 2,503,9 hectares, with lattitude ranging from 7°12’ to 7°21’ S and longi- tude from 112°36’ to 112°36’ E, is located in the east coast of Surabaya (Fig. 1). This MPA has a total of 15 mangrove species (Fauziah, 2011) and with domi- nated mangroves being Avicenia Marina sp., Rhizopora sp., Brugueira gymnorrhiza sp., Brugueira cylindrica sp., and Nypa Fruticans, sp. Bird species have been found in this area.
Evident human disturbance to the this MPA occured because people have long used this area as a brackish water pond area for shrimp and milkfish.
Besides, this area is surrounded with residences.
This area is also developed as mangrove tourism
was promoted for their considerable economic ben- efit for local people.
Data and Sources
This study uses data topographical map scale of 1:
25,000, google earth satellite images in 2012, 2014, and 2015. Amount of change (in hectares) of man- groves area, which was used as a proxy for coastal land utilization, were estimated in a GIS environ- ment.
Flowchart of the study
The methods employed in this study involved the
analysis of the coastal land utilization, evaluation of production of aquaculture, and the prediction of trend of coastal land area change. In the analysis of the coastal land utilization, it is used software ARCVIEW 3.3 for processing the google earth sattelite images. In evaluation the production of aquaculture, it is used linear regression. In the pre- diction of trend of coastal land area change, it is used software STELLA 10.1 for modelling the changes of coastal land areas. Flowchart of this study are set in Fig. 2.
Regresion analysis for aquaculture production The method used to analysis of aquaculture produc- tion is the linear regression. The linear regression equation is as follows:
Y = a + b X Where :
a = constant, X = independent variable
b = coefisien of regression, Y=dependent variable To get the value of a and b is used the least squares method below.
Y – b X a = n
n X Y – X. X b = n X 2 – ( X) 2 Where :
n = number of data Fig. 1. Map of research location in the city of Surabaya
Problems regarding the utilization of coastal land in MPAof ECS
Analysis of utilization of coastal lands in MPA
Evaluation of production of aquaculture in ECS
Prediction of coastal land area change in MPA
Sintesis for finding alternatif solutions in MPA of ECS
Coastal resources management in MPAof ECS
Fig. 2. Flowchart of the study
X = number of variabel value of X
Y = number of variabel value of Y
Desain of dynamic model for land area changes The methodology used for this model are as follows (Prasita et al., 2017) : (1) literature; (2) data collection;
(3) formulation of the model; (4) the calibration and validation; (5) the determination of sensitive vari- ables (leverage); (6) simulation. Literature studies is conducted to determine the basic equation model- ing and variable - related variables. The collection of data is to get the data used as the basis for simula- tion modeling. Data obtained from the previous studies and agencies.
Formulation of the model is the process of creat- ing / designing a model using Stella software. This process begins with making a simple model in the form sub models, and then combined into one inte- grated dynamic system. Each of these sub-models must be calibrated and validated in advance with the existing data. For sub-model that is valid/cor- rect, then examined the variables that are sensitive (leverage).
Furthermore, the resulting model used for the simulation. Results of the simulation models used to explain the phenomenon as well as the prediction of the condition of land area changes in the MPA of ECS. In the simulation diagram almost all the de- tails needed to write a mathematical definition of variables. After the simulation diagram is made then proceed with arranging mathematical equa- tions that describes the relationship between factors influencing changes in coastal land. Desain of dy-
namic model for land area changes is showed in Fig 3.
Mangrove Land Changes Model Equation and codes of the model are as follows :
MPA_land(t)=MPA_land(t-dt)+ (Addition_
Land_Area- Reduction_Land_Area) * dt INIT MPA_land = 2068.8
INFLOWS:
Addition_Land_Area= MPA_land+Natural_ALA OUTFLOWS:
Reduction_Land_Area= MPA_land+Natural_RLA Abrasion_factor = 1
Abrasion_Speed = GRAPH(TIME)
(0.00, 1.81), (1.00, 1.81), (2.00, 1.81), (3.00, 1.81), (4.00, 1.81), (5.00, 1.81), (6.00, 0.3), (7.00, 0.3), (8.00, 0.3), (9.00, 0.3), (10.0, 0.3), (11.0, 2.30), (12.0, 2.30), (13.0, 2.30), (14.0, 2.30), (15.0, 2.84), (16.0, 2.84), (17.0, 2.84), (18.0, 2.90), (19.0, 2.90), (20.0, 2.90), (21.0, 3.10), (22.0, 3.10), (23.0, 3.30), (24.0, 3.40), (25.0, 3.50), (26.0, 3.60), (27.0, 3.60), (28.0, 4.00), (29.0, 4.20), (30.0, 4.40) Accretion_factor = 1
Accretion_Speed = GRAPH(TIME)
(0.00, 56.8), (1.00, 56.8), (2.00, 56.8), (3.00, 56.8), (4.00, 56.8), (5.00, 56.8), (6.00, 21.2), (7.00, 21.2), (8.00, 21.2), (9.00, 21.2), (10.0, 21.2), (11.0, 26.5), (12.0, 26.5), (13.0, 26.5), (14.0, 26.5), (15.0, 28.0), (16.0, 28.0), (17.0, 28.0), (18.0, 28.3), (19.0, 29.6), (20.0, 29.9), (21.0, 31.0), (22.0, 32.1), (23.0, 32.8), (24.0, 34.3), (25.0, 35.8), (26.0, 37.6), (27.0, 39.4), (28.0, 40.9), (29.0, 43.4), (30.0, 46.0) Natural_ALA= Accretion_Speed*Accretion_factor
Natural_RLA = Abrasion_Speed*Abrasion_factor
Fig. 3. Model desain of natural land area changes.
Results and Discussion
Assesment of the Mangrove Protected Areas Utilization
Since the implementation of regional regulations Surabaya No. 12 year 2014, there are zoning in the MPA of ECS, namely the main zone, a buffer zone and a limited use zone (Bappeko, 2016). At the time of the adoption of legislation, in accordance with the conditions of the zone has not been specified zone, in the main zone are still a lot of pond used by as many as 403 people with an average area of 1.14 ha pond. The total areas of the ponds in the main zone are around 462.30 ha (Prasita et al., 2017). This con- dition indicates an improper zoning. Actually, proper zoning is crucial in formulating an economi- cally, socially and environmentally plan for the management and development of coastal waters (Andalecio and Cruz, 2010).
Surabaya city government has tried to restore the main zone as the main protected area for planting mangrove. This condition can be seen from satelite image of the Year 2012 to the Year 2014 (Fig.4), i.e.:
several ponds in the protected area has been re- stored its main functions into mangrove forests, as many as 10 plots with an average area of 2.25 ha.
Kesalahan penerjemahan Nevertheless there are still people who convert mangrove land into cultivated land for aquaculture. This condition makes this area vulnerable to environmental damages. Removal of mangrove forest diminishes their capacity to attenu- ate waves, trap sediment and accumulate organic
matter (Wesenbeeck et al., 2015). This condition can be seen from satelite image of the Year 2014 up to 2016 in Fig. 5 (Prasita et al., 2017).
Monitoring of spatial conservation areas in the field has not been strictly enforced so that the com- munity still utilizes this weakness to convert man- groves to the designation of ponds. The community also does not know the limits of land use zone to be protected. This is indicated from the results of inter- views from several people indicate that zoning MPA of ECS not communicated well to the public.
Fathoni one of the owner of the pond about 8 Ha stated that there has never been socialization of zon- ing boundaries in ECS. This is also conveyed by Suwito, field coordinator of mangrove management in Wonorejo, that the division of zones (main pro- tected zone, buffer zone and utilization zone) has not been socialized to the pond farmers in ECS, es- pecially Wonorejo Sub district. Suwito also agreed if the zonation is primarily for mangrove rehabilita- tion with ponds (silvofishery model), reforestation of the main protected zone, and provide buffer zone. According to them, the function of MPA is to (1) keep the land un-aberated, (2) limit the develop- ers to enter (reclamation), (3) anticipate the occur- rence of Rob (flood), (4) increase the income of the community and fishermen (fish, crabs, shrimps).
Bengen and Dutton (2004) stated that the silvo-fish- ery model applied in Indonesia is favoured for man- grove rehabilitation programmes. It involves a simple level of silvo-fishery where a channel carry- ing tidal water with a bund surrounds an area of replanted Rhizophora spp. mangroves. Mangrove utilization through the silvo-fishery pattern is more
(A) (B)
Fig. 4.Satellite imagery showing changes in pond plots to mangrove in 2012 – 2014, (A). September 2012, (B).
June 2014.
(A) (B)
Fig. 5.Satellite imagery showing changes in pond plots to mangrove in 2014 – 2015, (A). June 2014 September 2012, (B). September 2015.
beneficial than mangrove utilization by traditional or intensive ponds.
The land change occurs because some coastal lands are owned by the community so they can change the land at will. Currently, the local govern- ment has difficulty in the cost of pond restoration into mangrove forests. The government should re- place the land so that the government can manage the area into a conservation area in full. Such condi- tions are experienced by Malaysia. Olaniyi et al.
(2012) states that the implications of coastal land use change in Malaysia are far reaching thus put the country at a crossroad of critical decision of whether to conserve or convert a coastal area for economic
Strong political will of the government to defend the land as a conservation area is needed because around the region has become an elite area for settlements. Bengen and Dutton (2004) state that mangrove forest provide food and habitat for prawns, crabs, and fish at critical phases of their life cycle. When mangroves are lost, fisherment suffer substantially decrease catches of prawns and many fish spesies. Olaniyi et al. (2012) states the achieve- ments of these objectives (coastal restoration and conservation) in the long term, however, require strong political will by the goverment at all level.
In addition, the government should pay attention to the community with appropriate compensation so that the land owners can also accept the area as a protected area. There is a win-win process between the government and the landowner community. In coastal governance, political leaders are often chal- lenged with equitable allocation of limited resource users (Andalecio and Cruz, 2010).
Dharmawan et al. (2017) recommended a science- based win-win solution to solve such problems.
They try to implement their concept for the fishery management problem in Segara Anakan. The sci- ence-based approach provided a high-quality win- win solution (with full assessment of national and international scientific resources). They proposed a good governance approach which could have helped to push the science-based win-win solution by integrating different actors and their interest.
However, his research shows the failure of imple- mentation of the concept because the local govern- ment does not want to follow the ideas offered. The local government still decides on the fishery man- agement in accordance with its own willingness by not accommodating the interests of other stakehold- ers so that no win-win solution. Therefore, the suc-
cess of MPA management is determined by under- standing and relationships among stake holders.
Greater concern for and understanding of the rela- tionships among stakeholders are needed for decen- tralization of coastal zone management (Siry, 2006).
Assesment of production of brackish water ponds in the MPA of ECS
Besides as a conservation area, MPA is also used by the community for brackish water ponds but pond conditions are currently less productive. This can be seen from the data production of brackish water ponds cultivation in Surabaya for 20 years in the year 1996-2015 due to brackish water pond cultiva- tion only at ECS (Table 1).
Table 1. Pond area, farmers, and production of brackish water aquaculture from year 1995-2015 in Surabaya
No Year Pond Pond Production
Areas Farmer of Ponds (Ha) (People) (Ton)
1 1995 5,073 1,926 6,210
2 1996 673 1,991 6,122
3 1997 3,218 1,941 6,641
4 1998 3,218 2,003 7,040
5 1999 3,218 1,905 7,251
6 2000 3,355 1,819 7,377
7 2001 3,355 1,410 7,546
8 2002 4,837 1,056 7,697
9 2003 4,134 1,389 8,081
10 2004 4,053.42 1,423 8,405
11 2005 3,954.15 1,514 8,825
12 2006 4,394.86 1,239 8,573
13 2007 4,394.86 1,425 7,886.15
14 2008 3,065.11 1,126 8,198.9
15 2009 3,065.11 1,126 8,608.7
16 2010 3,051.17 911 9,043.3
17 2011 3,139.66 901 7,932.84
18 2012 3,139.66 901 7,593.18
19 2013 3,139.66 901 6,053.66
20 2014 3,139.66 901 6,054.66
21 2015 3,139.66 900 6.785,15
Sources: Surabaya in Figures/Surabaya dalam Angka (BPS, 1995-2015)
In the year 1996-2015, the development of brack- ish water aquaculture production follows the re- gression equation Ypt1 = 242.59 X + 5903.85. In 1996 the production of brackish water fish farming at 5903.85 tons and increasing steadily at a rate of 242.59 tons/year until 2006. In 2007, production of
brackish water aquaculture drop in the value of 7886.15 tons nevertheless climbed back with the rate of 388.12 tons/year for 4 years following the regres- sion equation Ypt2 = 388.12 X + 7463.95 up until production reached 9043.3 tons and after the start of 2010, production fell continuously until now fol- lows the regression equation Ypt3 = - 785, 65 X + 9692.47. This can occur due to the development of brackish water aquaculture production has stag- nated and weakened due to lack of good aquacul- ture environmental conditions, among others : the high salt content (lack of fresh water), low quality shrimp seed. Currently milkfish is a fish that is suit- able to be cultivated in the area.
With such conditions, slowly vast number of ponds and fish farmers decreased following the lin- ear regression equation Ytm = -3.75 X + 3520.35 for a broad decline and Yptm = -64.36 X + 2066 for fish farmers. In detail, shown in Table 1 and Fig. 6. Such aquaculture conditions, they need a breakthrough to resolve the issue. As an alternative solution, first of all, Bosem Wonorejo can be proposed as a source of fresh water so that the requirement can be ful- filled ponds. Second, cultivate shrimp and milkfish seed quality. Third, Management of in and out of freshwater and seawater. The prawn production will be increased, due to better seawater is ex- changed into the pond, and the chances for prawn
diseases spreading are decreased (Xiu-Zhen, L., 1999).
The utilization of the area for the pond is not good at this time so the improvement of MPA envi- ronment is needed or redesign the zone of man- grove conservation area so that its utilization can be done sustainably. Bengen and Dutton (2004) state that the concepts of mangrove forest protection and the mangrove forest rehabilitation basically provide validation and understanding that mangrove eco- systems urgently require protection and proper management in order to make them sustainable.
Assesment of land change utilization using dynamic model
Utilization of land conversion in conservation areas was originally used to expand the main protected areas. However, the utilization may change depend- ing on the supervision of the manager. Some com- munities have taken advantage of some for brackish water ponds as described previously. To find out the extent to which changes in land area made a dynamic model of land changes naturally which will be described below.
This dynamic model is designed to see patterns of land change naturally. The land change is caused by accretion (land addition) and abrasion (land re- duction). Some of the data used for the model are land area, accretion area and accretion area which are calculated from satellite image data processing presented in Table 2 and accretion rate and abrasion rate data are shown in Table 3. The accretion rate in the MPA of ECS is greater than the abrasion rate so that extended lands appear. To see how much the extended lands are, then modeling is done. This modeling can also predict the area of land for the next few years.
The Running Model results are shown in Fig. 7.
In the picture, there are five variables of the results.
These variables are accretion speed, abrasion speed, Fig. 6.Pond areas, farmers, and production of brackish
water aquaculture fish versus times for 20 years.
Table 2. Summary of land area calculation in the MPA of ECS
No. Date of satelit Land area Land change Accresion Abrasion
image (m2) area (m2) area (m2) area (m2)
1. 22 April 1996 20688044.69 n.a. n.a. n.a.
2. 22 October 2002 23988043.55 3299998.86 3408401.44 108402.58
3. 11 September 2007 25031686.24 1043642.69 1061079.19 17436.50
4. 23 May 2011 25997855.97 966169.73 1058223.76 92054.03
5. 6 June 2014 26753562.17 755706.20 841029.41 85323.21
6. 27 July 2015 27082290.56 328728.39
additional land area, reduction area, and mangrove protected area (MPA land). The five variables are expressed by various colors, accretion rate, abrasion rate, additional area, reduced area, and mangrove protected area are respectively stated in purple, blue, red, orange and green.
At the first five years, the abrasion rate and accre- tion rate are constant at 1.81 hectares/year and 56.80 hectares/year respectively. Accretion rate is much greater than the rate of abrasion so that this condition causes the protected area to increase. This condition can also be seen in Table 4.
After that, the condition of abrasion rate and ac- cretion fluctuate tend to increase. This also affects the increase and reduction of existing land in the MPA of ECS so that the protected area also tends to increase. This condition is used to predict land ac-
quisition over the next 10 years that begins in the 21st to the 30th year.
This validation is necessary to see the truth of the dynamic model making of natural land change in the MPA of ECS. Validation of this model uses real data of coastal land conditions in the MPA of ECS with linear regression interpolation for several years. In the validation process, the model requires accurate data from the current analysis results as well as with previous research data. Table 5 shows the comparison of real data with the model data of land change in the MPA of ECS.
In Table 5, it is shown that the difference of model data with real data is not large, except in the data of the past year, the data area on the model is lower than the real condition. So the dynamic model of land change can be said to be good because the deviation is relatively small.
Table 3. Calculation result of rate of land area change in the MPA of ECS
No. Year Land area Rate of land Rate of land area
change (m2) area change change (Ha/year)
(m2/year)
1 1996-2002 3299998.86 549999.8 54.99
2 2002-2007 1043642.69 208728.5 20.87
3 2007-2011 966169.73 241542.4 24.15
4 2011-2014 755706.20 251902.1 25.19
5 2014-2015 328728.39 328728.4 32.87
Fig. 7. Graph of the result of the running model for natural land area changes in the MPA of ECS
Based on the above model, although the overall land in the MPA increases, but the eroded land also tends to increase. This condition should also be overcome with mangrove rehabilitation or building breakwater a feasible location. The success rate of mangrove rehabilitation effort was researched by
Table 4. Result of running model for land area changes in the MPA of ECS for 30 Years
Year Abrasion rate Acresion rate Land areas Land areas Land area (Ha)
(Ha/Yrs) (Ha/Yrs) caused by caused by
abrasion (Ha) accresion (Ha)
1 1.81 56.80 2,125.60 2,180.59 2,123.79
2 1.81 56.80 2,180.59 2,235.58 2,178.78
3 1.81 56.80 2,235.58 2,290.57 2,233.77
4 1.81 56.80 2,290.57 2,345.56 2,288.76
5 1.81 56.80 2,345.56 2,400.55 2,343.75
6 0.30 21.20 2,399.04 2,419.94 2,398.74
7 0.30 21.20 2,419.94 2,440.84 2,419.64
8 0.30 21.20 2,440.84 2,461.74 2,440.54
9 0.30 21.20 2,461.74 2,482.64 2,461.44
10 0.30 21.20 2,482.64 2,503.54 2,482.34
11 2.30 26.46 2,505.54 2,529.70 2,503.24
12 2.30 26.46 2,529.70 2,553.86 2,527.40
13 2.30 26.46 2,553.86 2,578.02 2,551.56
14 2.30 26.46 2,578.02 2,602.18 2,575.72
15 2.84 28.03 2,602.72 2,627.91 2,599.88
16 2.84 28.03 2,627.91 2,653.10 2,625.07
17 2.84 28.03 2,653.10 2,678.29 2,650.26
18 2.90 28.30 2,678.35 2,703.75 2,675.45
19 2.90 29.60 2,703.75 2,730.45 2,700.85
20 2.90 29.90 2,730.45 2,757.45 2,727.55
21 3.10 31.00 2,757.65 2,785.55 2,754.55
22 3.10 32.10 2,785.55 2,814.55 2,782.45
23 3.30 32.80 2,814.75 2,844.25 2,811.45
24 3.40 34.30 2,844.35 2,875.25 2,840.95
25 3.50 35.80 2,875.35 2,907.65 2,871.85
26 3.60 37.60 2,907.75 2,941.75 2,904.15
27 3.60 39.40 2,941.75 2,977.55 2,938.15
28 4.00 40.90 2,977.95 3,014.85 2,973.95
29 4.20 43.40 3,015.05 3,054.25 3,010.85
Final 4.40 46.00 3,050.05
Table 5. Validation of dynamic model of land change
Year Land areas Land area Selisih
from image from the processing Model (Ha)
(Ha)
1996 2,068.80 2,068.80 0.00
2002 2,398.80 2,398.74 -0.06
2007 2,503.17 2,503.24 0.07
2011 2,599.78 2,599.88 0.10
2014 2,675.36 2,675.45 0.09
2015 2,708.23 2,700.85 -7.38
measuring density and distribution of Rhizophora and colonization of Avicennia marina in Karimunting and Penibung Bay (Akbar et al., 2017). The result showed that the breakwaters built in Karimunting and Penibung Bay were successful to reduce the amount of coastal erosion up to 70% within 22 years. This breakwater breaking experience can be applied in MPA mangrove tourist sites.
Other mangrove management is colaborative management. Colaborative management is manage- ment that can accommodate a variety of problems faced by the mangrove ecosystem by the govern- ment as the leading sector with the highest priority on the management of the ecological dimension (Schaduw et al., 2012).
In addition, the predicted modeling results can also be estimated to increase the MPA area. With the increasing MPA area, the mangrove area also in-
creases so the main zone area will increase as well.
This will strengthen the presence of MPA. How- ever, currently the addition of MPA land area is not used to expand MPA (Prasita et al., 2014).
Conclusion and Recomendations
Research result conclude several points and sug- gest recomendations for better management of MPA. First, the zoning boundaries of the region are clarified, in the zone plan in the field must corre- spond to on the map. This is to prevent the commu- nity from using rehabilitated mangroves. Second, from the results of linear regression model, produc- tion tends to decrease. This shows the condition of the community pond environment is less good.
Therefore, this area should be rehabilitated to obtain better production and the area needs to be rede- signed MPA zonation. Third, from the model it is seen that the area of land tends to increase every time so that the extended land should increase the area of protected area and MPA area is increasingly widespread.
Recommendation should be made for eco- friendly pond cultivation, such as silvofishery. The local government should educate the public to use the land in the MPA to be environmentally friendly.
In addition, because there are many different condi- tions of coastal land use in the field with zoning plans that have been made by local governments, the MPA needs to be redesigned to accommodate the interests of the community and the local gov- ernment.
Acknowledgements
The authors thank to the director of Indonesian higher education and research institutions (Ristekdikti), chief of board of the research and ser- vice affairs at Hang Tuah University, as well as the anonymous peer reviewers. The research con- ducted for this paper was supported by the Ministry for Research, Technology and Higher Education, Republic of Indonesia under funding scheme of the Excellent Research of Higher Education (Penelitian Unggulan Perguruan Tinggi) in 2016.
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