Zoning Seagrass Protection in Lap An Lagoon, Vietnam Using a Novel Integrated Framework for Sustainable Coastal Management

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https://doi.org/10.1007/s13157-021-01504-8 COASTAL WETLANDS

Zoning Seagrass Protection in Lap An Lagoon, Vietnam Using a Novel Integrated Framework for Sustainable Coastal Management

Nam Thang Ha¹ · Tien Dat Pham² · Thi Thuy Hang Tran¹

Received: 31 May 2021 / Accepted: 7 October 2021

Abstract

Seagrass is a key factor of the nature-based solution to climate change impacts, however, this resource has been lost and degraded worldwide in both of area and habitats. Protection of extant seagrass often requires a zoning approach to fit the local conditions and to identify the spatial prioritization. In this study, we integrated the state-of-the-art machine learning model (CatBoost) and Sentinel-2 multi-spectral imagery for mapping the extent of seagrass meadows, combined with the multi-criteria evaluation (MCE), fuzzy logic (Sigmoidal membership function), and analytic hierarchy process (AHP) technique in a GIS database to score the ecological protection zoning of seagrass ecosystem in Lap An lagoon, Vietnam.

Seagrass map retrieved from Sentinel-2 multi-spectral imagery was used as the constrain factor, whilst salinity, water depth, substratum, and distance to aquaculture sites were conditioning factors. Our results presented accurate mapping of seagrass meadows in the study site (scores of overall accuracy, precision, and F₁ are 0.93, 0.90 and 0.92, respectively) and indicated 22.05 ha (scores 0.66—0.99) in high, 18.63 ha (scores 0.33—0.66) in medium, and 10.80 ha (scores 0—0.33) in low priority of protection in the southern and the eastern southwest parts of the lagoon, and the areas closed to the aquaculture sites, respectively. Our novel integrated approach to map the priority zones is useful for sustainable protection and management of seagrass meadows and provides a framework to strengthen the application of remote sensing and GIS-based techniques for further conservation of seagrass globally.

Keywords Sentinel-2 · GIS · Multi-criteria evaluation · Fuzzy · AHP · Seagrass · Conservation

Introduction

Seagrass is a flowering marine plant, widely distributed across climatic regions. The spatial distribution ranges from either the intertidal or subtidal zones, providing numerous important ecosystem services such as water filtering, wave attenuation, feed supply, and sediment trapping (Nordlund et al. 2016, 2018). Seagrass can store carbon in their meadows and is considered as a Blue Carbon ecosystem (Fourqu- rean et al. 2012; Alongi et al. 2016; Gullström et al. 2018).

In recent years, seagrass ecosystem is recognized as one of

the most important components of nature-based solutions in dealing with climate change impacts (Lovelock and Duarte 2019). Despite a relatively lower rate of carbon sequestration than mangrove forests and salt-marsh, seagrass meadows are capable of storing more carbon than terrestrial forests (Bouillon et al. 2007; Howard et al. 2014), implying their values in the global carbon cycle to reduce greenhouse gas emissions. However, the habitats of seagrass meadows are narrowed and largely degraded (Dat Pham et al. 2019; de los Santos et al. 2019; Ha et al. 2020), leading to an urgent requirement of mapping seagrass meadows extent and habitat protection in the world (Nordlund et al. 2018).

During the recent decades, the distribution of seagrass was successfully mapped using various satellite sensors and advanced mapping techniques considering both field-based and remote sensing-based methods. The latter approach (i.e.

remote sensing-based method using satellite images and machine learning (ML) classification algorithms) promises a wide-broad application and provides accurate mapping and evaluation of seagrass ecosystem changes (Traganos

* Nam Thang Ha

[email protected]; [email protected] Thi Thuy Hang Tran

[email protected]

1 Faculty of Fisheries, University of Agriculture and Forestry, Hue University, Hue 530000, Vietnam

2 Department of Earth and Environmental Sciences, Macquarie University, Sydney, New South Wales 2109, Australia

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et al. 2018; Dat Pham et al. 2019; Ha et al. 2020; Zoffoli et al. 2020; Ha et al. 2021). In addition, several policies have been adopted for the restoration and conservation of seagrass ecosystems (van Katwijk et al. 2016; Paulo et al. 2019;

Rezek et al. 2019; Aoki et al. 2020). These efforts, however, are not always successful due to the deficiency of capital resources (Wilson and Forsyth 2018), extreme climate phenomena (Aoki et al. 2020), the dynamics of the aquatic environment, and reliable spatial planning (van Katwijk et al. 2016), and the differences in local requirements fit- ting to various conditions of seagrass meadows (Unsworth et al. 2019). Among the unsolved challenges, we consider the necessity of a rational planning framework (Griffiths et al. 2020a, 2020b), which provides a priority ranking of seagrass protection. To do this, a geographic information system (GIS)-based approach which integrates spatial information and spatial analysis into a decision-making framework of multi-criteria evaluation (MCE) would be essential, which may be the most popular form in practice (Gemitzi et al. 2010; Nelson and Burnside 2019). Given evaluation problems, the MCE requires the input data such as the conditioning factors, which need to be standardized in a range of 0 -1 and be calculated by their impact weights. Analyti- cal hierarchy process (AHP) (Saaty 1987, 2003) is a well- known technique (Chandio et al. 2013; Pendred et al. 2016;

Portman et al. 2016; Lembo et al. 2017) for the calculation of impact weight whilst a fuzzy logic approach is popular for the standardization of the input factors into GIS-based planning models (Teh and Teh 2011; Hattab et al. 2013;

Dias et al. 2020). The addition of AHP and fuzzy membership function in the process of MCE are considered and may improve the reliability during the zoning process (Feizizadeh et al. 2014; Shahabi et al. 2016).

The current literature reveals a wide range of applications of the MCE, providing the rationale and accurate marine spatial planning in various zoning problems (Li et al. 2014;

Nguyen et al. 2016; Pallero et al. 2017). The most recent and relevant research works employed the use of the multi- criteria analysis with ordered weighted averaging (OWA) (Zabihi et al. 2019) for citrus cropland site selection; the MCE (Radiarta et al. 2008) for Japanese scallop culture; the MCE and a fuzzy measurement (Jiang and Eastman 2000) for industrial planning; the multi-criteria decision-making (Mousavi et al. 2015) for artificial coral reef zoning; the MCE using the weighted linear combination (MCE-WLC) (Malczewski 2000; Gemitzi et al. 2010; Dapueto et al. 2015;

Mousavi et al. 2015; Thomas et al. 2019) for macro-algae cultivation, offshore fish farming, and environmental problems evaluation. To the best of our knowledge, however, there is no study that applied the integrated techniques for seagrass protection (Andalecio 2010; Griffiths et al. 2020a, 2020b), which identifies the ranking of priority zoning adapted to various local conditions. Therefore, this research

aims to fill the current gap in the literature by integrating spatial information and analysis for seagrass protection zoning. We integrated the state-of-the-art ML model (i.e. the CatBoost (CB) algorithm) for seagrass mapping using Sen- tinel-2 multi-spectral imagery and the MCE-WLC combined with the AHP and the fuzzy logic (Sigmoidal membership function) in a GIS database to map the priority ranking of various seagrass meadows for protection in Lap An lagoon, Vietnam through a ranking score in this study. Our novel integrated approach demonstrates the successful identification of seagrass distribution/ area and the spatial distribution of the priority zones and provides a reliable approach for better conservation strategies in dealing with climate change impacts.

Materials and Methods

Study Site

We conducted the current study in Lap An, a semi-enclosed lagoon located in the southern coastal region of Thua Thien- Hue Province, Vietnam (Fig. 1). The surface area is approximately 15 km² and has an average water depth of 2.5 m (Ha 2012). Both seawater and freshwater exchange through an outlet and the three springs in the southern and the eastern parts of the lagoon (Fig. 1), creating the brackish-water environment and supporting native aquatic resources (Ha 2012). Lap An is a well-known lagoon for not only tourist activities but providing a diverse livelihood of aquaculture and fish harvesting, which was attributed to surrounding mangrove forests and seagrass meadows as important ecological habitats, sustaining the wealth for thousands of local fishermen around the lagoon (Ha 2012). However, studies indicated a significant decrease of seagrass area in Lap An lagoon, from 250 ha in 1995 to 120 ha in 2004 (Nguyen et al.

2004), which was affected by aquaculture, tourist activities and resulted in the scatter distribution of extant seagrass meadows (Ha et al. 2012). The current situation suggests: (i) a need to update the seagrass distribution/ area in the lagoon;

and (ii) a requirement of seagrass zoning in the lagoon due to the diversity of various anthropogenic activities and the limitations in capital resources of the local government. The successful implementation of the zoning and protection of seagrass will increase the abundance of the meadows, contribute to the restoration of the nursery ground of aquatic animals, and positively impact the livelihoods of the local fisherman.

Field Survey

The field surveys for data collection of environmental parameters (red diamond in Fig. 1) and the ground-truth

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points (GTPs) location of aquaculture sites (green circle in Fig. 1), substrata (including seagrass (yellow asterisk) and non-seagrass (red diamond)) were conducted in July 2012 and in July 2016, respectively. Substrata were visually identified on site using samples collected from a Petersen grab (Riddle 1989; Hails 2006). Salinity was measured using a handheld refractometer on-site. We used a sonar scanner Humminbird 587ci HD to measure the water depth, inter- polating the measured data points to make the bathymetry map in Lap An lagoon. We recorded the position of aquaculture sites (58 GTPs), in which each aquaculture site was marked with one location point, the boundary of seagrass (511 GTPs) and non-seagrass substratum (120 GTPs) using a handle global positioning system (GPS) Garmin Etrex 30 (accuracy in ± 2 m).

Ecological Ranges of Conditioning Factors in Lap An lagoon

In this study, the factors of water depth, salinity, substratum, and distance to aquaculture site, which could potentially affect the spatial distribution and the health of seagrass meadows (Nguyen et al. 2004; Tussenbroek et al. 2006; Col- lier et al. 2014; Herbeck et al. 2014; Tupan and Uneputty

2018; Cullain et al. 2018), were selected as the conditioning factors in the MCE-WLC analysis.

The ecological ranges, which indicate the ranges of conditioning factors at which seagrass grows healthy, were identified to be used as the references to determine the transition points (A, B, C, D) in the fuzzy membership function. Based on the published document (Table 1) and our monitoring data in Lap An lagoon ((Ha et al. (2012) and field survey in 2016), the minimum–maximum and the relative optimal ranges were determined for all seagrass species Halodule pinifolia (Miki) Hartog, 1964, Halophila ovalis (R.Brown) J.D.Hooker, 1858, Thalassia hemprichii (Ehrenberg) Ascherson, 1871 (Turland et al. 2018) in Lap An lagoon.

Methods

We proposed a novel integrated framework for generating a zoning map for seagrass protection involving three main steps (Fig. 2): (1) conducting the field survey and mapping the extent of seagrass meadows using the state-of-the-art approaches in a binary format to make the constraint map, whereas seagrass and non-seagrass pixels were set to values of 1 and 0; (2) calculating fuzzy scores for conditioning factors (substratum, salinity, water depth, and distance to aquaculture site) and corresponding weights using the

Fig. 1 Lap An lagoon – study site (ρ_Red—ρ_Green—ρ_Blue composition of the Sentinel-2 multi-spectral imagery acquired on 13 April 2016)

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techniques of the Sigmoidal fuzzy membership and the AHP, respectively; (3) applying the MCE-WLC, then combining the constraint and conditioning maps using raster calculator in the GIS environment to make the zoning map of seagrass protection.

Seagrass Binary Mapping Using ML Model

Satellite Image Used for Seagrass Binary Mapping A cloud- free Sentinel-2 scene was downloaded from the GLOVIS website (https:// glovis. usgs. gov/) at the level 1C, projection WGS 84 UTM 48 N, and was subset to the boundary

of the study site (Table 2). The image was atmospherically corrected with the ACOLITE application (Vanhellemont 2016, 2019) using the parameters in Table 1 (Supplemen- tal material). Due to the inconsistency between the satellite acquisition time and the local low tide, our study site was considered as the submerged area during the acquired time of Sentinel-2 satellite. Therefore, the only three remote sensing reflectance bands at the wavelengths of 492 nm (ρ_Blue), 560 nm (ρ_Green), and 665 nm (ρ_Red) were used for further processing step of seagrass mapping.

Table 1 Ecological ranges of seagrass in Lap An lagoon

1 The minimum and maximum thresholds at which seagrass is able to survive in the shallow water of the coastal area

Factor Minimum—Maximum¹ Optimal range Source of data

Water depth (m) 0—~ 5 0.5—2 (Nguyen et al. 2004; Ha 2012)

Salinity (‰) 5—~ 35 30—32 (Nguyen et al. 2004; Collier et al. 2014; Tupan and Uneputty 2018) Substratum Sand, muddy-sand, mud Sand, muddy sand (Nguyen et al. 2004; Tussenbroek et al. 2006; Ha 2012)

Minimum distance to

aquaculture site (m) ~ 200—300 ~ 1000—1500 (Lin and Fong 2008; García-Sanz et al. 2010; Herbeck and Unger 2013; Herbeck et al. 2014; Cullain et al. 2018)

Fig. 2 Novel integrated framework for seagrass protection zoning in the study

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CB Algorithm Released in 2018, the CB is an advanced boosting algorithm, which has been successfully applied for both the numerical and categorical data with reliable performance in a wide range of applications (Kang et al. 2019;

Prokhorenkova et al. 2019; Pham et al. 2020; Luo et al. 2021;

Ha et al. 2021). The CB method has the advantages of the ensemble learning-based family and was invented with the ordered boosting during the construction of decision trees (Prokhorenkova et al. 2019). This new feature reduces the data leak in the training phase and therefore has the potential to improve the prediction accuracy. Considering other boosting algorithms such as the extreme gradient boosting (XGB), the gradient boosted decision tree (GBDT), the CB consists of fewer hyper-parameters with the four most important hyper-parameters of depth, number of trees, learning rate, and L₂ leaf regression, which helps to reduce the computation time for the hyper-parameters tuning and performance.

CB Model Hyper‑Parameter Tuning The CB model consists of the hyper-parameters (depth, number of trees, learning rate, and L₂ leaf regression) which should be optimized to retrieve the best performance. We used the GridSearchCV with five-fold cross-validation (CV) in the library Scikit- learn (Pedregosa et al. 2011) to optimize the best combination of the hyper-parameters (Table 2, Supplemental material).

Seagrass Mapping Using CB Model Using the GTPs of seagrass and non-seagrass substrata, a total number of 1516 pixels was selected to test the CB performance for the binary seagrass classification in Lap An lagoon. The dataset was divided into 60% (909 pixels) for the training set and 40%

(607 pixels) for the testing set using a random splitting in the Scikit-learn library (Pedregosa et al. 2011). A binary seagrass map was then generated for the study site and the map was resampled to a ground sampling distance (GSD) of 30 m to work with the data of conditioning factors.

Spatial Analysis, Fuzzy, and AHP Implementation

Spatial Analysis We employed the interpolation using the inverse distance weighting (IDW) technique and the

proximity analysis to compute the raster maps of all conditioning factors (substratum, salinity, water depth, and distance to aquaculture site) into a GIS database in the SAGA GIS environment (Conrad et al. 2015). All raster maps were resampled to a GSD of 30 m in the projection of WGS84 UTM 48 N.

Fuzzy Membership Function Selection and Implementa‑

tion Fuzzy logic is a solution for not completely true/ false problems, in which the solution is interpreted in a range of fuzzy sets (values of 0—1) using the membership functions (Volosencu 2020). With the advantages of simplicity in logic system structure, low memory in implementation, accepting various types of input data, fuzzy logic has been widely applied in different research fields with success (Hat- tab et al. 2013; Jones and Cheung 2018; Kambalimath and Deka 2020; Mittal et al. 2020). For a given dataset, the use of fuzzy membership functions provides a reliable transfor- mation of input factors into a unitless form ranging from 0 to 1 (Feizizadeh et al. 2014). Several forms of membership functions are available, however, the most popular forms include the linear, the Sigmoidal, and the J-shaped functions.

In this study, the fuzzy membership function was selected from the following criteria: (1) the ranges of conditioning factors that support the optimal ecological conditions for seagrass; (2) the transitions of A, B, C, D points in the membership function that fit the optimal ranges of the conditional factors. Various thresholds of the transition points (A, B, C, D) for input parameters were determined based on the key informant interviews at the Department of Fisheries Man- agement (Hue University) using data of ecological ranges (Table 1). The Sigmoidal membership function (Figs. A1a and A1b, Supplemental material) was selected as a result of conducted interviews and suggestion in the literature (Jiang and Eastman 2000; Shahabi et al. 2016) to standardize the raster maps of substratum, salinity, water depth, and distance to aquaculture site in SAGA GIS (Conrad et al. 2015).

The transition values, which cover the ecological ranges (Table 1), were cross-validated with the local fishermen during the field surveying conducted in 2016. In the current study, the “increase” and the “decrease” types of the Sigmoidal membership function were both considered to fit a variation of the input variables.

Analytic Hierarchy Process (AHP) The AHP uses the Saatty matrix (Saaty 2003) to compare the importance among the input factors and to calculate their impact weights. In this work, we employed the AHP to calculate the impact weights for all conditioning factors using a Saaty matrix (Forman 1990; Saaty 2003) and an excel interface (Goepel 2013, 2018). To compare the importance among these factors, three key informants, including an expert in the Department of Fisheries Management, a local fisherman,

Table 2 Sentinel-2 imagery used for binary seagrass mapping in Lap An lagoon

Acquisition

date Satellite acquisition local time

Spatial resolution (m)

Cloud cov-

erage (%) Local time of low tide

13 April

2016 10:23 AM 10 0 First low tide:

4:36 AM Second low

tide: 5:26 PM

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and a fourth-year student in the Department of Fisheries Management, were involved in the process. To avoid the objectivity of the human judgments, and hence reduce the uncertainty during the interview, we selected the informants from a wide range of multi-disciplinary, ages, and working experiences to deliver an objective judgment and improve the accuracy of calculated weights. The judgment scales, on the other hand, might significantly contribute to the consistency and the allocation of priority values in the comparative matrix (Franek and Kresta 2014). With the advantages of the simplicity in implementation, stability in performance, and low variation in priority allocation (Saaty 1994; Franek and Kresta 2014; Vinogradova-Zinkevič et al. 2021), the linear scale was used as the judgment scale to keep the nature of the assessment and increase the certainty of the weight valuation. We explained the AHP method to the members, then conducted various independent discussions to produce a reliable comparison in the Saaty matrix. The accuracy and consistency of the calculated weights were evaluated using the metrics of consistency ratio (CR) and consensus indica- tor (S*) (Goepel 2013). A CR, which is smaller than 0.1, is sufficient for a good consensus whilst the S* should be high as possible.

Priority Zoning

Weighted Linear Combination (WLC) In the WLC, the score values ranging from 0—1 were calculated for each pixel, whereas 0 and 1 are equivalent to “not suitability” and

“entirely suitability”, respectively. The score value of each pixel was calculated from the following formula:

where:

WLC_i: the score at pixel i c_j: the fuzzy membership values

w_j: the weight of the factor j. Total weight equal to 1 The map of WLCi was combined with the constrain map using the raster calculator in the GIS environment to produce the final map of priority zoning for seagrass protection.

Results

Binary Classification of Seagrass Meadows Mapping Using the CB model, the distribution of seagrass was successfully classified at a level of high accuracy (Table 3). The precision, F₁ scores reached over 0.9 and the Kappa coefficient 0.85, determining the confidence and reliability of

(1) WLC_i=∑n

jc_j×w_j

the CB model for seagrass mapping in Lap An lagoon. Our results denoted large meadows distributed in the southern and the southern-west parts whilst scatter meadows were observed in the eastern and the northern parts of Lap An lagoon (Fig. 3). Seagrass area was approximately estimated as 51.48 ha in the year 2016.

Spatial Analysis of Conditioning Factors

The transition values (A, B, C, D) were inferred from the optimal ranges (Table 1) for the Sigmoidal membership function (Table 4).

Our results from the spatial distribution analysis of input factors (Fig. 4a, b, c, d) indicated a diversity in ranges of the “water depth” (0.3—3.2 m), “salinity” (30.9—36.6 ‰),

“distance to aquaculture site” (44—1569 m), and “substratum” (sand, muddy-sand, mud). The fuzzified scores (Fig. 4e, f, g, h) illustrated the spatial patterns of the suitability for each factor. It is noted that areas with high fuzzified scores (0.75—0.99) distributed close to the southern part and the edge of the lagoon, identifying the suitable habitats for the seagrass study site. In contrast, areas with low fuzzified score (< 0.5) are located in the middle part, except for a specific distribution of the factor “distance to aquaculture site” (Fig. 4h).

Weighting Factors Using AHP

Using the Saaty matrix (Tables 5, 6, 7) as the input data for AHP, our results revealed that the factor “distance to aquaculture site” was ranked more important, and its weight (0.395) is higher than those of the remaining conditioning factors. Further, we observed the “substratum” factor plays an important role as the “water depth”, and it is twice higher than that of “salinity”. As a result, the “substratum” factor is likely less important than the “distance to aquaculture site”

(0.239) but contributes more than “water depth” (0.198) and

“salinity” (0.168) factors (Table 8).

A low value of the metric (CR 0.022 < 0.1, Table 8) indicated a reliable assessment in the comparison of the factors.

In addition, the AHP showed a very high value of S* (99.1, Table 8), reflecting a strong agreement and consistency among the three people involved, and therefore presented the reliable weights for each factor in the study site.

Table 3 Model performance for seagrass binary mapping

Overall accuracy (OA) 0.93 Kappa coefficient 0.85

Precision Recall F₁

Seagrass 0.90 0.93 0.92

Non-seagrass 0.96 0.93 0.94

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Seagrass Protection Zoning

Using the binary seagrass map (consisting of the Boolean values of 0 for non-seagrass and 1 for seagrass) and the fuzzy data, we calibrated the scores for generating the seagrass protection zoning map in Lap An lagoon. Of the seagrass extent, approximately 43% of seagrass meadows were in high priority of protection, accounting for 22.05 ha with the scores of 0.66—0.99, about 36% in the medium zone, equaling to 18.63 ha with the scores of 0.33—0.66, and 21%

in low priority of protection, making up to 0.33 score value.

Of 43% the highest rank for protection, seagrass meadows distributed far to the southern part, whilst more than 50% of the remaining meadows distributed in the western, northern, and eastern parts of the lagoon (Fig. 5). In addition, it

Fig. 3 Binary seagrass map derived from the CB model in Lap An lagoon

Table 4 Transition values at A, B, C, D points for Sigmoidal membership function in Lap An lagoon

1 Substratum codes with 1 for sand, 2 for muddy-sand and 3 for mud Parameter Sigmoidal type Transition values

A B C D

Water depth (m) Decrease 0.5 0.5 2 2.5

Substratum¹ Decrease 1 1 2 3

Distance to aqua-

culture site (m) Increase 300 1100 1500 1500

Salinity (‰) Decrease 30 30 33 35

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Fig. 4 Spatial variation (a, b, c, d) and scores (e, f, g, h, the scores go from 0 unsuitability (red) to 0.99 entirely suitability (blue)) of water depth, salinity, substratum, and distance to aquaculture site factors

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is noted that the spatial distribution of seagrass with a low ranking score distributed closer to the aquaculture sites or

tourist activities (Fig. 5, the western and eastern parts of the lagoon), showing the consistency with the Saaty matrix (Tables 5, 6, 7) and the calculated weights (Table 8) of the factors.

Discussion and Policy Implications

The current study is the first attempt for seagrass protection zoning by integrating both remote sensing and GIS-based approaches in Vietnam. Our novel proposed framework accurately identifies the position and the area of seagrass meadows using the Sentinel-2 imagery and the advanced CB ML model. Of 51.48 ha of seagrass meadows extent, 43% is classified as a high priority for protection, located mainly in the southern part of the lagoon (Fig. 5) which is far from the aquaculture sites, no lime production, and the environmental parameters are in optimal ranges for the seagrass’ growth. In addition, Thalassia hemprichii is the dominant species of the meadow observed in this area, a species strongly tolerates the changes of the salinity and the current in the mouth of the lagoon (Nguyen et al. 2004).

To the date of this study, seagrass area is in decreasing trend with approximately 80% area loss in comparison to the year 1995 (250 ha) (Nguyen et al. 2004), which might be attributed to a large number of aquaculture ponds and the changes in the lagoon substratum and water depth. Aqua- culture activities, which are characterized by intensive and semi-intensive shrimp farms discharged a large amount of untreated pond wastewater and mud into the lagoon. These uncontrolled releases result in a higher concentration of suspended solids (SS) in the water column, which directly prevent seagrass from photosynthesis and create a mask of SS over the meadows. In addition, the acceleration of aquaculture mud potentially affect the lagoon substratum and water depth, leading to a more shallow area and a dominated muddy sand substratum in the East and West shores (Fig. 4a and c) narrowing the spatial habitats of seagrass. Despite an important conditional factor which characterizes the sea- garss distribution in the context of global warming, salinity is less weighted in this study. Salinity variation (Fig. 4b), which was adjusted by the three springs (Hoi Mit, Hoi Dua, Hoi Can) in the South and Southwest parts, varied not much (33.8‰—35.2‰) in almost area and this variation is in the tolerance range of seagrass in the lagoon. Despite a need for further data collection and analysis, the hypothesis of seagrass loss in Lap An lagoon is consistent with calculated weights of conditional factors in Table 8. Therefore, the zoning adopted in this work is reliable to the current conditions of seagrass meadows and other factors in Lap An lagoon.

Regarding the AHP, the most challenges are the consensus in comparison among the participants and the number of the participants in the AHP. A number larger than ten

Table 5 Saaty matrix for conditioning factors in Lap An lagoon from the first member

Substratum Water depth Distance to aquaculture site

Salinity

Substratum 1 1 1/2 2

Water depth (m) 1 1 1/2 1

Distance to aquaculture site (m)

2 2 1 2

Salinity (‰) 1/2 1 1/2 1

Table 6 Saaty matrix for conditoning factors in Lap An lagoon from the second member

Salinity

Water depth (m) 1 1 1/2 2

2 2 1 2

Salinity (‰) 1/2 1/2 1/2 1

Table 7 Saaty matrix for conditioning factors in Lap An lagoon from the third member

Salinity

Water depth (m) 1 1 1/2 1/2

2 2 1 2

Salinity (‰) 1/2 2 1/2 1

Table 8 Weight of input conditioning factors and their evaluation metric

Parameter Weight Metric

S* (%) CR

Substratum 0.239

Water depth (m) 0.198 99.1 0.022

Distance to aquaculture

site (m) 0.395

Salinity (‰) 0.168

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participants might lead to disagreement among the members for input parameters in the Saaty matrix, whilst a number below two participants might lead to an overlooking of the importance and confuse the Saaty matrix. We acknowledged this point and attempted to fill the gap by selecting three participants from various disciplines, involving researchers in ecologist and aquaculture sectors together with the fishermen and the student in the major of Fisheries Management.

The metrics (Table 8) indicated a good consensus among the participants and the comparison in the Saaty matrix was unbiased with a high S* (99.1%) and a low value of the CR (0.022) which is much smaller than the CR’s threshold (0.1). With an exception of the factor “distance to aquaculture site” (much more important in the Saaty matrix), other conditioning factors (“substratum”, “salinity”, and “water depth”) were evaluated a bit similarity in importance with

closer values in weight (Table 8). A fixed number of participants in the AHP for different scenarios might not be feasible and still be debatable in the literature (Schmidt et al. 2015; Şahi̇N and Yurdugül 2018; Darko et al. 2019).

The consistency and consensus metrics can be stable after a few respondents interviewed, which are potential as the functions of the problems being discovered. The increment of the sample size (i.e.the number of participants) might be recommended, however at the user own risk since this addition might contribute nothing but the noise to the AHP and produce a large variation in priority allocation. In this case study, the output of the interview (i.e.priority allocation of different conditional factors) is clearly described and the experienced participants are able to identify the true priority with the linear scale. Our metrics indicate a sufficient

Fig. 5 Protection zoning map for seagrass meadows in Lap An lagoon

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assessment and reliable results with designed number of participant for the case study in Lap An lagoon (Table 8).

To address the zoning technique, the available options of the MCE include the Boolean, the WLC, and the ordered weighted average (OWA) approaches for various zoning practices. The Boolean technique is simple, however may result in absolute zoning and lead to the wrong classification of “suitability” or “unsuitability” areas. In contrast, the OWA requires the calculation of the weights for each of the pixels, which results in the map of weight rather than a sin- gle weight in the WLC. The OWA, therefore requests more observation input data, a complex computation, and higher cost during the zoning. As a result, we developed an integrated approach using the WLC combined with the AHP and fuzzy logic which is reliable and suitable to the recent conditions in Lap An lagoon. The proposed framework is simple, fast, and open-sourced based computation, which is rational for extending to various regions with the target of seagrass protection zoning.

The acceleration of anthropogenic activities in the coastal areas and extreme weather conditions are implied as to the sources of seagrass’ habitat destruction (Unsworth et al.

2019), leading to the loss of various seagrass ecosystem services (Macreadie et al. 2015; Nordlund et al. 2018), and may result in unstable livelihoods of worldwide fishermen (Hejnowicz et al. 2015; Bujang et al. 2018). A similar sce- nario of seagrass loss might be appropriate to the case of Lap An lagoon with additional attributes of aquaculture and tourism activities. Long-term management and conservation of seagrass ecosystem, therefore is highly necessary toward a natural-based adaptation (Seddon et al. 2020) or

“umbrella effect” strategy (Weng et al. 2015; Thornton et al.

2016), which emphasize on the optimization of the selecta- ble conservation and has been the target of various part- ners in the world. However, it is noted that conservation is a multi-step tactic (Roberge and Angelstam 2004), and zoning should be the first step, providing a priority ranking that fit the local capital resources. Functional zoning is a popular terminology in the tasks of marine spatial planning (Fang et al. 2011), aquaculture (Sanchez-Jerez et al. 2016), coastal zone integrated management (Fang et al. 2018), commu- nity-based management of aquatic resources (Castrejón and Charles 2013) heading to the most suitable usage of natural resources. Downscaling to seagrass ecosystem, the species have been involved in different projects of transplantation site selection using transplantation index (Pirrotta et al.

2015) or habitat suitability modeling (Valle et al. 2015), shallow seagrass restoration using light and boat density modeling data (Hotaling-Hagan et al. 2017), strengthening the local management using a species distribution model (Adams et al. 2016) whilst the seagrass growth potential modeling, environmental and management data were combined to discover the suitable restoration sites (Thom et al.

2018). Despite a wide range of applied techniques, our work is different from the published papers. We combined a novel framework consisted of state-of-the-art satellite sensor and ML model for seagrass detection and a reliable integrated zoning model to support the protection purpose of extant seagrass meadows in the coastal areas. The presented results provide novel ideas for seagrass protection using the zoning approach and practical methods for in-depth management of both aquatic resources and concerned activities. The overdevelopment of aquaculture and tourism activities was reported as the driving factors of seagrass degradation in the world (Daby 2003; Davenport and Davenport 2006; Herbeck et al. 2014; Cullain et al. 2018). Despite, management of those activities have not usually come into force due to the pressure of economic development and a low capacity of the local manager in responding to a rapid change in resource exploitation and controversy in the conservation (Sanchez- Jerez et al. 2016; Unsworth et al. 2018). Our study site might be a typical example of the complexity in management with diverse economic activities among aquatic ecosystems in low protection and debate of balance in economic development and natural resource conservation, which will need further in-depth studies in the future.

In spite of the success of the developed method, the current study may come with a limitation. A near-synchroniz- ing of data collection acquired from remote sensing-based approach using Sentinel-2 imagery in April 2016, and the environmental parameters collection obtained between 2012 and 2016 may result in a slight variation of the input parameters. We noted this challenge and attempted to conduct all the sampling and satellite image collection acquired in the same summer of these years in which the meteorological and environmental parameters were not much different in a couple of years ((Ha 2012); field survey in 2016). In addition, the uncertainty of the AHP results is not fully ana- lyzed in this study due to the complexity in the structure and the input data types of assessment models (Eskandari and Rabelo 2007; Durbach and Stewart 2012; Toth and Vacik 2018). We also acknowledged this point and attempted to increase the certainty during the interview (i.e. using the linear judgment scale and independent interviews with different respondents) and validate the quality of the AHP using the standard CR and S* metrics, which secured for the objectivity and the consistency during the AHP analysis (Table 8).

The novel proposed method, though is very potential and merit further investigation in future studies to other lagoons for seagrass protection zoning and other Blue Carbon ecosystems (i.e.mangroves and salt-marshes) worldwide. Our approach is simple in the framework, reliable in applied techniques, very cost-effective solution, and open-source in both the dataset and selected GIS/ ML models. Therefore, it will be practical and beneficial to both the manager and

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scientist, supporting long-term conservation and adaptation policies in the context of climate change globally.

Conclusions

In the current study, we proposed a novel integrated framework using the state-of-the-art satellite imagery (Sentinel-2) and ML model (CB) to derive accurate mapping of seagrass meadows extent (scores of OA, precision, and F₁ are 0.93, 0.90, and 0.92, respectively) and the GIS analysis using the MCE—WLC, AHP, and Sigmoidal fuzzy membership function in a GIS database to successfully rank the priority protection of seagrass meadows in Lap Ap lagoon, Vietnam.

Accordingly, seagrass area was approximately estimated as 51.48 ha in 2016, of which 22.05 ha was identified in highest priority (scores ranging from 0.66—0.99), 18.63 ha in medium (score ranging from 0.33—0.66), and 10.8 ha in low priority (score ranging from 0.00—0.33). The southern part of the lagoon contains a large number of meadows in first protection.

Our results indicate the successful integration of the ML model, the multi-spectral remote sensing data, and the GIS-based method for seagrass protection zoning for the first time in Vietnam. The proposed method is simple, open-source, reliable with free-of-charge satellite sensor and the availability of the modern toolset using ML and GIS models and therefore, should provide a practical and continuous approach for designed plans of protection and/

or restoration of seagrass meadows worldwide. In addition, our proposed framework might benefit the managers in the conduction of efficient spatial planning, towards sustainable aquatic resources conservation and policy implementations in the coastal regions.

Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s13157- 021- 01504-8.

Acknowledgements The authors are grateful to the University of Agriculture and Forestry, Hue University for the assistance during the fieldwork.

Author Contributions Conceptualization, N.T.H; Methodology, N.T.H;

Software, N.T.H, T.D.P; Validation, N.T.H, T.T.H.T; Formal Analysis, N.T.H; Writing-Original Draft Preparation, N.T.H; Writing-Review &

Editing, N.T.H, T.D.P, T.T.H.T. All authors have read and agreed to the final version of the manuscript.

Data Availability Not applicable.

Code Availability Not applicable.

Declarations

Conflicts of Interest The authors declare no conflict of interest.

Ethic Approval Not applicable.

Consent to Participate Not applicable.

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