PLOS ONE

RESEARCH ARTICLE

Mangrove forest classification and

aboveground biomass estimation using an
atom search algorithm and adaptive neuro-
fuzzy inference system
Minh Hai Pham ID1*, Thi Hoai Do1, Van-Manh Pham ID2, Quang-Thanh Bui2

1 Vietnam Institute of Geodesy and Cartography, Ha Noi, Viet Nam, 2 Center for Applied Research in
a1111111111 Remote Sensing and GIS (CARGIS), Faculty of Geography, VNU University of Science, Vietnam National
a1111111111 University, Hanoi, Thanh Xuan, Ha Noi, Viet Nam
a1111111111
a1111111111 * phamminhhai.vigac@gmail.com
a1111111111

Abstract

OPEN ACCESS

Citation: Pham MH, Do TH, Pham V-M, Bui Q-T Background

(2020) Mangrove forest classification and
aboveground biomass estimation using an atom
Advances in earth observation and machine learning techniques have created new options
search algorithm and adaptive neuro-fuzzy for forest monitoring, primarily because of the various possibilities that they provide for clas-
inference system. PLoS ONE 15(5): e0233110. sifying forest cover and estimating aboveground biomass (AGB).
https://doi.org/10.1371/journal.pone.0233110

Editor: Francisco Martı́nez-Álvarez, Pablo de

Olavide University, SPAIN
Methods
Received: November 9, 2019
This study aimed to introduce a novel model that incorporates the atom search algorithm
Accepted: April 28, 2020 (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classifica-
Published: May 21, 2020 tion and AGB estimation. The Ca Mau coastal area was selected as a case study since it
has been considered the most preserved mangrove forest area in Vietnam and is being
Copyright: © 2020 Pham et al. This is an open
access article distributed under the terms of the investigated for the impacts of land-use change on forest quality. The model was trained
Creative Commons Attribution License, which and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multi-
permits unrestricted use, distribution, and
spectral information from the SPOT image. In addition, feature selection was also carried
reproduction in any medium, provided the original
author and source are credited. out to choose the optimal combination of predictor variables. The model performance was
benchmarked against conventional methods, such as support vector regression, multilayer
Data Availability Statement: Sentinel 1A is
available for download at https://scihub.copernicus. perceptron, random subspace, and random forest, by using statistical indicators, namely,
eu/, and the other is available at DOI:10.5281/ root mean square error (RMSE), mean absolute error (MAE), and coefficient of determina-
zenodo.3715333 (host by zenodo.org). tion (R2).
Funding: This research was funded by Vietnam
Academy of Science and Technology under grant
number VT-UD.08/18-20, which belongs to the
Results
National Program on Space Science and
Technology (2016 - 2020). The results showed that all three indicators of the proposed model were statistically better
Competing interests: The authors have no conflict than those from the benchmarked methods. Specifically, the hybrid model ended up at
of interest. RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation.

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Conclusion
From the experiments, such hybrid integration can be recommended for use as an alterna-
tive solution for biomass estimation. In a broader context, the fast growth of metaheuristic
search algorithms has created new scientifically sound solutions for better analysis of forest
cover.

Introduction
Biomass, which includes above- and belowground biomass, is a critical component of carbon
budget accounting and carbon monitoring, especially under the context of climate change
[1,2]. Aboveground biomass (AGB) includes both live and dead material; estimation of the
AGB of live trees has been more prominent in recent research. Accurate biomass quantifica-
tions are a prerequisite for a better understanding of the impacts of deforestation and environ-
mental degradation on climate change [3]. The estimation of biomass is now crucial, as it is
considered an essential source of energy in many countries. Technically, there are two biomass
estimation methods. The destructive methods require tree cutting and further indoor weighing
procedures [4]. These methods are limited to smaller areas and are usually employed to mea-
sure the biomass of sample plots that can also be used as ground truth samples. On the other
hand, non-destructive measures take advantage of spatial technology to estimate AGB from a
distance through backscatter or reflectance signals [1].
Mangrove forests cover a small portion of the global land area [5], but their carbon-rich
ecosystems play a crucial role in sustaining the livelihoods of coastal communities [6], protect-
ing coastal lines and inner land from storms and tsunamis [7], offsetting anthropogenically
produced carbon dioxide, and contributing to carbon export to the ocean [8]. However, man-
grove forests are threatened by human interactions that claim forest cover for aquaculture and
agricultural activities. The estimated global loss of mangrove forests is approximately 0.16 to
0.39% [9], which poses a significant risk to the total carbon emission rate because of the con-
siderable proportion of carbon storage in mangrove forests [9,10]. The surveillance of man-
grove biomass is, therefore, crucial for estimating the potential carbon stored in these forests
for global emission reduction programs.
Remote sensing (optical, radio detection and ranging- radar; light detection and ranging-
Lidar) probably provides the best alternative for estimating AGB on a large scale and enables
repetitive and rapid assessment of biomass over large areas relatively quickly and at a low cost,
providing a more spatially comprehensive measure of forest biomass variation. Radar remote
sensing enables surveying operations in all weather conditions [11–13], and it is usually used
in combination with optical imageries to obtain complementary information about mangrove
structure and biomass [1,14]. Among the radar bands, AGB can be effectively calculated by
using the high-frequency L-band and P-band because of their penetration capability, as
explained in the studies of [1,11,15] through the use of ALOS PALSAR data. From a global per-
spective, both active and passive remote sensing have become vital sources of data for mapping
the spatial distribution of vegetation [16,17].
There is a growing body of research on the application of remotely sensed imageries,
machine learning algorithms, and geospatial information technology in AGB estimation
[18,19]. The underlying scientific background is to understand the correlation between back-
scatter and the reflectance of remotely sensed data and live biomass over a given area [20,21].
The methods for AGB estimation are diverse. For example, the application of inversion of the

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

PROSAIL model estimates AGB by using leaf dry matter content and leaf area index [22]. The
combination of vegetation indices with radar data has also been investigated in several works
[1,23]. From reviewing the literature, several types of research were found that employed
machine learning models to estimate the AGB. As examples, statistical and data-driven
approaches have been proposed, such as multiple regression in the study of [13,24], geographi-
cally weighted regression [25], and support vector regression and random forest in [1,23,24].
Recently, metaheuristic algorithms have gained considerable popularity because they are
capable of searching for the optimal parameters of classifiers in image classification and disas-
ter susceptibility mapping by solving objective functions that are differently defined case to
case. Three typical types consist of physically based, swarm intelligence, and evolutionary algo-
rithms that mimic the behaviors or mechanisms of natural events to mathematically model
artificial applications [26–28]. New models are being examined for their simplicity, flexibility,
and ability to solve complex nonlinear problems. However, few of these models can be found
for biomass estimation in such a way that the optimization algorithm supports the improve-
ment of the performance of the classifiers that are generally trained by conventional methods.
Currently, new networks and models are continuously being developed for various applica-
tions, many of which are available as open-source libraries [13]. To the best of our knowledge,
the use of metaheuristic algorithms in biomass studies is still limited.
Although there is a vast number of studies on applications of machine learning algorithms
in biomass estimation, there are no models that fit all problems. Moreover, the search for opti-
mal machine learning models is crucial to contribute to global knowledge in the field of forest
management. This study aimed to investigate a novel combination of atom search optimiza-
tion algorithms and adaptive neuro-fuzzy inference systems in classifying mangrove forests
and estimating AGB. Ca Mau Province, a coastal area in southern Vietnam, was selected as a
case study because of its diverse ecosystems and its role in protecting the coastal zone. This
hybrid model was validated by using common statistical indicators, namely, root mean square
error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) and was
benchmarked by regression models that had been used for mangrove studies, such as multi-
layer perceptron (MLP), support vector regression (SVR), random subspace (RS) and random
forest (RF). The geospatial database was processed by Quantum GIS (QGIS), the segmentation
process, and SPOT-6, and its derived indices were analyzed by using PCI Geomatica 2018 Ser-
vice Pack 2. The Sentinel-1A imagery was processed by Sentinel Application Platform (SNAP)
from the European Space Agency, and the model was coded in MATLAB R2018b. SVR, RF
and multilayer perceptron were implemented in Weka version 8.3.

Study area and data

Description of the study area
In Vietnam, mangrove forests are mainly distributed in the northeastern and northern delta
and the central and southern delta, with the densest area in the U Minh National Park. As the
importance of mangrove forests has become widely recognized, the area has been regrown and
expanded through national and international projects. However, the transformation of man-
grove forests into aquaculture and other economic activities continues in some areas, and it
makes the coastal zone more susceptible to natural hazards such as salinity intrusion, drought,
coastal erosion and flooding [2,3].
Ca Mau Province is on the southernmost coast of Vietnam, which is located on the Mekong
Delta and extends between the latitudes of 8˚34’N and 9˚33’N and longitudes of 104˚43’E and
105˚25’E (Fig 1). Ca Mau is characterized by many rivers and canals and low and flat terrain,
and it is periodically flooded. Influenced by the tropical monsoon climate near the equator, the

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Fig 1. The study area in Ca Mau Province and the distribution of 158 sampling plots are presented spatially on
this map. Background spatial data were collected from https://gadm.org/ and processed by the authors.
https://doi.org/10.1371/journal.pone.0233110.g001

regional weather can be divided into two seasons: the rainy (from May to November)
and dry (from December to April) seasons. The study area is characterized by an average
temperature of 26.5˚C, an annual average rainfall of approximately 2,360 mm, an annual aver-
age evaporation of 1,022 mm and an annual average moisture of 85.6% (http://www.camau.
gov.vn).
The Ca Mau mangrove forest is the second most pristine forest in Vietnam, both in terms
of species composition and biomass, with an entire area of approximately 69,000 ha [2]. It is
located mostly in the Ngoc Hien and Nam Can districts, and the remaining area is situated in
the Dam Doi, Phu Tan, Tran Van Thoi, and U Minh districts. Most of the forest area is in the
Ca Mau Cape Biosphere Reserve (41,862 ha). Surrounded by the sea and 249 km of coastline,
the forest is considered an erosion barrier. The forest is also the green lung of the whole south-
eastern region, which plays a role in climate harmonization, ecological balancing, and environ-
mental protection. Ca Mau has diverse mangrove species, among which the most saline-
tolerant plants are in Avicenniaceae (Avicennia alba, Avicennia marina) and Rhizophoraceae
(Rhizophora apiculata, Rhizophora mucronata, Bruguiera gymnorrhiza), Lumnitzera racemosa,
and Excoecaria agallocha. Among these, Rhizophoraceae is the most popular species, so the
forest is also called Rhizophora forest. Another aspect relating to the management of specific
uses of certain types of mangrove forest includes the zoning of the mangrove forest into func-
tional zones. In this regard, six distinct types of forest have been defined, namely, natural Rhi-
zophora forest, natural mixed Avicennia/Rhizophora forest, naturally regenerated Avicennia
forest, Rhizophora plantation forest (mainly Rhizophora apiculata and Rhizophora mucro-
nata), Avicennia plantation forest (mainly Avicennia alba and Avicennia marina), and other
mangroves forest and shrubs.

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Data used
Predictor variables from Sentinel-1A and SPOT-6 imagery. There are numerous studies
on the uses of active radar at the L-band (1 GHz � 2 GHz), C-band (4 GHz � 8 GHz), and P-
band (300 MHz � 1 GHz) in estimating the AGB of mangrove forests. However, the selection
of the radar bands is subject to the availability of input data from either commercial satellites
or free sources. In this paper, the Sentinel-1A C-band (ground range detected product; inter-
ferometric wide swath mode; 250 km swath width; 5 × 20 m spatial resolution; VV: vertical
transmit–vertical receive, and VH: vertical transmit–Horizontal receive dual polarization) was
acquired on March 23, 2015. High-resolution SPOT-6 satellite imagery (Satellit Pour l’Obser-
vation de la Terre 6—February 8, 2015, with 6 m multispectral resolution capability) data were
used to estimate the biomass of the mangrove forests in Ca Mau Province. The Sentinel-1A
and SPOT-6 multispectral data were acquired in February/March 2015 at the same time as the
field measurements were conducted. Since the spatial resolutions of Sentinel-1A (5 m x 20 m)
and SPOT-6 (6 m) are technically different, the SAR data were resampled in the multispectral
image to 6 m resolution by the Bilinear resampling technique, which computes new pixels
using linear interpolation. This process was implemented after the preprocessing of the raw
dataset by using PCI Geomatics 2018 software.
The Sentinel-1 data were preprocessed using the Sentinel-1 toolbox (S1TBX) embedded in
the SNAP desktop application (version 6) from the European Space Agency (http://step.esa.
int). The orbit file with accurate satellite and velocity information was also used for this step
and included (i) radiometric calibration of the backscatter representation of the reflecting
object (converted from digital number (DN) values to σo values); (ii) speckle filtering for
speckle suppression using the Lee adaptive filter (with a window size 7×7) [13]; (iii) terrain
correction using the DEM data at a 5 m spatial resolution from the Vietnam Ministry of Natu-
ral Resources and Environment to correct for the SAR geometric distortions; and projection of
the images into the WGS 84 coordinate system in the UTM zone 48N projection.
The multispectral SPOT-6 data (blue, green, red, near-IR) were obtained from optical satel-
lite sensors with a high spatial resolution of 6 meters. The study area had a cloud cover of less
than 10%. The multitemporal satellite image was calibrated, and the radiation/atmospheric
effects were removed by using the ATCOR (atmospheric correction) function integrated with
PCI Geomatics 2018 software. The processing consisted of three parts: (i) top-of-the-atmo-
sphere reflectance; (ii) haze removal and cloud masking; and (iii) ground reflectance atmo-
spheric correction [29]. These images were projected into the WGS84 coordinate system/
UTM zone 48N projection with ground-control points and orthorectified by using DEM data,
which ensured a geometric correction accuracy of approximately ±0.5 pixels.
Even though the limitation of the C-band in interacting with the more profound compo-
nents of the forest was mentioned, few works have investigated the potential uses of Sentinel-
1A with a variety of optical image indices [30,31]. This study is a continuation of research on
the C-band from Sentinel-1A in mangrove forest estimation, in which the combination of
polarizations was proposed, such as HH, HV, HH-HV, and HH/HV, as has been suggested in
many studies [1,13,15]. The structure of mangrove forests (open or closed) and the water level
conditions on the acquisition day influence the volume backscatter, double-bounce backscat-
ter and HH, HV, and VV polarizations. As a supplement, the optical imageries provide useful
information about mangrove conditions by transforming spectral bands to enhance the contri-
bution of the vegetation properties or chemical components of the leaves. A wide variety
of vegetation indices that differ from each other in their transformation equations and
required objectives were used in this study, as shown in (Table 1). Such indices have also been
suggested to have significant contributions to the overall AGB estimation, as in the studies of

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Table 1. Predictor variables from Sentinel-1A and SPOT-6.

No. Independent variables Name/Explanations Source
1. SPOT 6 Band 1: Blue (0.455 μm– 0.525 μm)
2. Band 2: Green (0.530 μm– 0.590 μm)
3. Band 3: Red (0.625 μm– 0.695 μm)
4. Band 4: Near-Infrared (0.760 μm– 0.890 μm)
5. Sentinel 1A VV (Vertical Transmit-Vertical Receive Polarizations, 3.75 to 7.5 cm wavelength)
6. VH (Vertical Transmit-Horizontal Receive polarizations, 3.75 to 7.5 cm wavelength)
7. AVERAGEvhvv (polarization average) [13]
8. DIFFvvvh (polarizations difference) [13]
9. MULTvhvv (polarization multiply) [13]
10. RATIOvvvh (Cross polarized ratio) [13]
11. Spectral indices from SPOT 6 ARVI (Atmospherically Resistant Vegetation Index) [32]
12. ATSAVI (Adjusted transformed soil-adjusted VI) [33]
13. AVI (Ashburn Vegetation Index) [32]
14. BWDRVI (Blue-wide dynamic range vegetation index) [34]
15. CI (Coloration Index) [35]
16. CIGREEN (Chlorophyll Index Green) [36]
17. CIRED-EDGE (Chlorophyll Red-Edge) [37]
18. CVI (Chlorophyll vegetation index) [38]
19. DVI (Difference Vegetation Index) [39]
20. EVI (Enhanced Vegetation Index) [40]
21. GI (Greenness Index) [41]
22. GLI (Green Leaf Index) [42]
23. GNDVI (Green Normalized Difference Vegetation Index) [43]
24. GSAVI (Green Soil Adjusted Vegetation Index) [44]
25. GRVI (Green Ratio Vegetation Index) [36]
26. I (Intensity) [35]
27. IF (Shape Index) [35]
28. MSAVI (Modified Soil Adjusted Vegetation Index) [32]
29. NDVI (Normalized Difference Vegetation Index) [45]
30. OSAVI (Optimized Soil Adjusted Vegetation Index) [46]
31. PBI (Plant biochemical index) [47]
32. PNDVI (Pan Normalized Difference Vegetation Index) [48]
33. PVI (Perpendicular Vegetation Index) [49]
34. RDVI (Renormalized Difference Vegetation Index) [50]
35. RI (Normalized Difference Red/Green Redness Index) [51]
36. SAVI (Soil Adjusted Vegetation Index) [52]
37. SIPI3 (Structure Intensive Pigment Index 3) [53]
38. TSARVI (Transformed Soil Atmospherically Resistant Vegetation Index) [32]
39. TSAVI (Transformed Soil Adjusted Vegetation Index) [54]
40. TVI (Transformed Vegetation Index) [32]
41. WDRVI (Wide Dynamic Range Vegetation Index) [55]
42. WDVI (Weighted Difference Vegetation Index) [33]
https://doi.org/10.1371/journal.pone.0233110.t001

[1,20,21,23]. Forty-two predictor variables were generated in Table 1 and the average values
(based on the centers of sample plots and plot sizes) were used as an input database for the
analysis workflow, as shown in Fig 3.

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Field survey dataset. The field survey was carried out from January to March 2015 in the
Ca Mau district and was authorized by the Mui Ca Mau National Park and local administra-
tion. The investigations followed the guidelines issued by the Ministry of Agriculture and
Rural Development with sampling plots with areas of 100 and 1,000 m2. The plot locations
were randomly selected across the study area and provided the best description and measure-
ment of the plot condition, canopy coverage rate, the total number of trees in each plot, aver-
age diameter, average cross-section, and tree heights. Fig 1 shows the coordinates of the center
of each plot. Afterward, the AGB was statistically estimated for each plot by using single tree
allometry and plot-aggregated allometry to quantify all measured trees in the plot. Two genera,
Avicenniaceae (Avicennia alba, Avicennia marina) and Rhizophoraceae (Rhizophora apiculata,
Rhizophora mucronata, Bruguiera gymnorrhiza), dominated the area, with an average density
of 2,830 trees per ha, an average diameter varying from 6.9 cm to 19 cm and an estimated bio-
mass between 40 and 340 Mg ha-1. The field estimation of AGB was calculated based on the
estimation equations from [1,56–58], as specifically shown in Table 2.

Background of the algorithms used

Adaptive neuro-fuzzy inference system
Since the first study of [59], the ANFIS has been widely used to solve numerous problems,
either in image classifications [27,60] or regression applications [61]. This method integrates
fuzzy inference into conventional neural networks and inherits the benefits of both. A typical
structure of the ANFIS is briefly described in Fig 2, and a more detailed explanation of each
layer set can be found in various studies, such as [59].
Rule k: IF x1 is Ck1 AND x2 is Ck2 . . .. AND xm is Ckm THEN Pi can be estimated through sev-
eral steps as described in the following layers:
Layer 1: This layer consisted of the training data and 42 associated predictor variables (as
presented in Table 1) or the remaining variables after the feature selection process. As a pre-
liminary step, a clustering method was used to define the optimal clusters for this training
dataset. These data were fed into Layer 2 with the following membership function:
1
mCji ðxi Þ ¼ xi cij 2bij ð1Þ
1þj aij
j

where i = 1:m, in which m is the number of input variables, and j = 1: k, where k is the number
of clusters as well as the number of rules in this study. The determination of k is carried out by
trial-and-error process; xi indicates the input variables (42 independent variables in Table 1 or
the variables after the feature selection process); Cji is the linguistic label, and mCji ðxÞ is

Table 2. Main allometric equations for the aboveground biomass calculation of each tree species.
Forest Type Allometric Equations
Avicenniaceae
Avicennia marina AGB = 0.308×DBH2.11
Avicennia alba AGB = 0.131×DBH2.46
Rhizophoraceae
Rhizophora apiculata AGB = 0.235×DBH2.42
Rhizophora mucronata AGB = 0.169×DBH2.46
Bruguiera gymnorrhiza AGB = 0.186×DBH2.31

(DBH is the diameter at breast height).

https://doi.org/10.1371/journal.pone.0233110.t002

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Fig 2. Adaptive neuro-fuzzy inference system.

https://doi.org/10.1371/journal.pone.0233110.g002

membership value that defines how much of factor (x) belongs to Cji . Pi is the predicted value;
and aij, bij, and cij are adaptive parameters to be adjusted by using the ASO.
Layer 3: The weights in this layer are calculated by using the following equation:
wj ¼ mCj1 ðx1 Þ � mCj2 ðx2 Þ . . . � mCjm ðxm Þ ð2Þ

j w
Layer 4: Weights are normalized in this step by w�j ¼ sumðw jÞ

Layer 5: This adaptive layer takes the sum of linear functions multiplied by the normalized
weights in the previous step. The equations are as follows:
P
fj ¼ w�j ðpj0 þ ðpkj xi ÞÞ ð3Þ

Pk
Final summation : Pi ¼ j¼1 j f ð4Þ

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1 2
RMSE ¼ ðP Oi Þ ð5Þ
n i

where pj0 and pkj are to be adjusted at the same time as aij, bij, and cij by the ASO. Oi is the
observed value (ground-truth value). n is the training size. Eq 5 is used as the objective function
for the ASO search. In general, the number of adaptive parameters is calculated based on the
number of input features and the number of clusters and is presented as follows: No. of
parameters = m � k � 3 + (m + 1) � k, in which 3 represents aij, bij, cij of the membership functions
as described in Eq 1. These parameters are tuned by the ASO as described in the next section.

Atom search optimization

First introduced by [62], the ASO is formulated based on the molecular dynamics in which all
atoms in search spaces interact with each other through attraction and repulsion forces. The

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

equilibrium stage is achieved when two forces are equal at a distance rij = 1.12σ. Technically,
the mass of a defined atom represents a solution, where a heavier atom is better than a lighter
one. Atom masses are influenced by the movement of all other atoms. The ASO can be simply
described as follows:
The objective function was defined in Eq 5 to minimize the RMSE of the regression func-
tion. To solve this minimization problem, an atom population of n in d dimensional space was
proposed. That was xi ¼ ½xi1 ; xi2 . . . :xid �, i = 1. . .n. where rij is the Euclidean distance between
atom i and atom j in d dimensional space. The positions of the atoms were randomly generated
in the d dimensional space xi(i = 1..n), and d is equal to the adaptive parameters of the ANFIS.
Atoms interact with each other by the force defined in Eq 6, and the total interaction is repre-
sented in Eq 7.
" #
24ε s 14 s 8
Fij ¼ rUðrij Þ ¼ 2 2ð Þ ð Þ rij ð6Þ
s rij rij

PN
Fi ¼ j¼1;j6¼i Fij ð7Þ

where U(rij) is the Lennard-Jones (L-J) potential between atoms i and j and σ is the length
scale that denotes the collision diameter. This potential U(rβ|) controls how the atoms interact
and therefore determines the positions of the atoms after each iteration.
The mass of the atoms is recalculated after each iteration by using Eq 8 and Eq 9:
Fiti ðtÞ Fitbest ðtÞ
Mi ðtÞ ¼ e Fitworst ðtÞ Fitbest ðtÞ
ð8Þ

M ðtÞ
mi ðtÞ ¼ PN i ð9Þ
j¼1 Mj ðtÞ

The interaction force Fi and acceleration are calculated after each iteration. From this stage,
the positions and velocities of all atoms are updated by using the following:
vdi ðt þ 1Þ ¼ randid vid ðtÞ þ adi ðtÞ ð10Þ

xid ðt þ 1Þ ¼ xid ðtÞ þ vid ðt þ 1Þ ð11Þ

where randid are random values between [0] and [1].

The searching process terminates when the predefined maximum number of iterations is
reached or the objective function reaches a smaller than desirable value. By integrating them
into the ANFIS, the optimal parameters of the ANFIS were defined for calculating biomass for
the whole study area.

Feature selection
Feature selection has considerable impacts on the performance of classification or regression
models through the elimination of irrelevant variables, handling multicollinerity [63] and
might effectively boost the operation [1,27]. It is also necessary to compare classifiers in an
unbiased manner. The simple Spearman correlation, which assesses monotonic relationships,
i.e., linear or nonlinear, has sometimes been used, but it has some specific limitations in the
nonlinear relationship. In some cases, even the Spearman coefficient indicates that two fea-
tures are highly correlated in low dimensional space; i.e., they are linear or nonlinear, which
can provide very different information in high-dimensional space. In this study, several feature

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

selection methods were examined, such as Relief Attribute Evaluation [64,65], subclass evalua-
tion [65], Correlation Attribute Evaluation [26,65], and the genetic algorithm [27,65]. Gener-
ally, the features were chosen by gradually adding more features until the performance of the
classifier started to drop. The use of cross-validation to calculate precision will probably reveal
that different classifiers choose different feature combinations. The features that are selected
by these methods will be examined by the proposed hybrid model, and the detailed assessment
is in the next section.

Performance assessment
This study aimed to find the best fit model but to ensure that the model would not be over-
fitted. This was achievable by estimating the expected prediction error. Therefore, to eliminate
overfitting problems, more training datasets were required, and different sampling methods
were used. Typically, there are three common ways to train and validate a model: (1) The
hold-out method randomly divides the points in the training set into roughly 70% for training
and 30% for validation, and (2) k-fold cross-validation (CV) randomly divides the training set
into k equal folds. In this case, previous studies have shown that ten-folds is the optimal num-
ber for this method [23]. (3) Leave p-out cross-validation with p equal to 1 is usually applied.
Each method requires a specific size of the training set, and in this case study, the sampling
size was large enough, so the hold out method was applied.
In general, for a regression study, the coefficient of determination (R2) is useful for explain-
ing the explanatory power of independent predictor variables. It is a common indicator that
has been used in all AGB estimation studies, such as in [1,13,15,23]. In other words, R2 gives a
sense of how well the model can explain the input dataset. Initially, R2 ranges from 0 to 1, but
negative values of (R2) are found in some cases. This situation occurs when the model is even
worse than a linear regression.
On the other hand, the RMSE (Eq 5) is useful for understanding the accuracy and precision
of the estimation model by comparing the predicted data (in this case, AGB) to the observation
data (in situ measures in each plot). The RMSE can be used either in classification or regres-
sion as an optimal objective function of the optimization process [27]. The drawback of this
value is that it is sensitive to large errors so that a preliminary screening of input data should
be performed to remove any outliers. Similarly, MAE also provides an average prediction
error with negatively oriented scores, which means lower values are better. Depending on the
actual dataset, the MAE and RMSE might vary differently and should not be used as compara-
tive indicators between estimation methods.
Finally, this hybrid model was benchmarked with machine learning methods that had been
used in previous mangrove studies, including MLP, SVR, and RF [20]. For this current dataset,
the parameters for SVR, including the kernel width (γ) and regularization (C), were defined
through a grid search. On the other hand, the determination of several trees impacted the
speed of search and accuracy of the result. From trial and error tests, 500 trees was the optimal
parameter for this selected RF method.

Proposed methodology for mangrove forest classification and aboveground

biomass estimation
This flowchart of Fig 3 shows the step-by-step procedure starting from image processing,
which was followed by the vegetation index calculation, and then the classification and AGB
estimation were implemented based on the input dataset from previous steps by using the pro-
posed machine learning method. Detailed explanations are described as follows:

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Fig 3. AGB estimation workflow.

https://doi.org/10.1371/journal.pone.0233110.g003

Step 1: This step involved the preprocessing of Sentinel-1A and SPOT-6 imagery, including
atmospheric correction, noise removal, image rectification, and image index calculation. This
step was implemented by using SNAP, which was developed by the European Space Agency,
and PCI Geomatics, a program that was created by the Canada Centre for Remote Sensing.
The output from this step was a layer stack of 42 predictor variables in the UTM-WGS 84 pro-
jection with a spatial resolution of 6 m for the SPOT-6 data and 10 m for the Sentinel-1A data,
which is resampled to 6 m by using SPOT-6 as the spatial reference. A short description of this
step was explained in the previous section.
Step 2: The inclusion or exclusion of certain predictor variables is subject to the nature of
datasets (forest type, growing stage, and geographical locations) and specific algorithms. This
means that, among all predictor variables that can be collected for AGB, different algorithms
might result in different combinations of variables based on the observed dependent ground
truth values. Therefore, this step is vital for filtering out redundant features that might have
negative influences on the predictive performance of the regression methods. This step is the
iteration process, in which features are alternatively selected by feature selection methods.
Each selected subset is used to run the proposed hybrid model and benchmarked algorithms
for comparison.
Step 3: The input data from Steps 1 and 2 were used for the AGB estimation and the classi-
fication of the mangrove forests. Since the long-term objective of this work was to quantify the
structure of each forest type in a time-series manner, the spatial distribution of each species
was required. The process was carried out with the use of the ASO-ANFIS for the forest cover
classification into six mangrove types, as mentioned in the previous section. The classified
map was overlaid on the AGB map to extract the AGB for each type.
Step 4: For any selected subset of features, the ASO searched for the optimal parameters of
the ANFIS, in which RMSE (Eq 1) was used as the objective function. The maximum number
of iterations and the dimensional space were defined by the structure of the ANFIS, as pre-
sented in Fig 2. The hybrid model started with the preliminary initialization of the model

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

parameters of the model (determination of atoms). The algorithm observes the movement of
atoms, calculates the fitness value for each, and identifies the best position of atoms. The pro-
cess iterates until the desirable condition is met. This process resulted in the smallest RMSE,
and the fine-tuned parameters of the ANFIS were used to estimate the AGB for the entire
study area in Ca Mau. For benchmarking, other methods were also examined against similar
training subsets for the performance comparison. A brief description of the ASO and ANFIS is
provided in the next section.

Mangrove forest classification

The main focus of this work was to monitor the changes in AGB across the most preserved
region in Vietnam, as mentioned in the previous section. For that reason, two independent
processes were implemented in parallel, which were the classification of forest cover and the
AGB estimation; the latter was the main focus. Therefore, the classification was a minor focus,
and result accuracies could be tolerated. In this regard, PCI Geomatics was used for image seg-
mentation with the determination of scale = 15, compactness = 0.5, and shape = 0.8 after sev-
eral trials. A total of 998 polygons, which were randomly selected, were manually assigned to
one of the six classes and used for model training, and 300 polygons were used for validation.
The classification was carried out by using the ASO-ANFIS, and the result was compared to
those from the most common classifiers, such as RF and SVM. The training and validation set
was built from segmented objects with associated attributes from SPOT data, such as multiple
spectral bands, NDVI, and texture analysis images [66]. The overall classification accuracy for
the ASO-ANFIS method was 86.7%, RF was 84.8%, and SVM was 82.6% (Table 3). The kappa
(kappa coefficients) for the ASO-ANFIS was 0.84, that for RF was 0.82, and that for the SVM
was 0.81. Fig 4 presents the results of the image classification by the ASO-ANFIS, including six
mangrove classes with different densities. The natural Rhizophora forest area was 3,436.42 ha,
the natural mixed Avicennia/Rhizophora forest area was 4,323.15 ha, the naturally regenerated
Avicennia forest area was 1,026.21 ha, the Rhizophora plantation forest area was 26,463,05 ha,
the Avicennia plantation forest area was 549.77 ha, and the other mangrove forest and shrub
areas were 33,121.39 ha. The study area retains vast mangrove resources and is a prestigious
World Biosphere Reserve in Vietnam.

Feature selection and AGB estimation

The training data (110 samples) and validation (48 samples) data were randomly split from
158 samples, with basic descriptive statistics such as mean = 181.9 Mg ha-1 and SD = 99.33 Mg
ha-1for the training set and mean = 159.9 Mg ha-1 and SD = 91 Mg ha-1 for the validation set.
Initially, the mean of the training set was higher than that of the validation set, and there was
more variation in the biomass for the training set than for the validation set.
On the other hand, the configuration of the proposed model, specifically the number of
weights to be tuned, is subject to the number of features that will be fed into the ANFIS.
Table 4 represents the features, which were selected by different feature selection methods
(implemented by using Weka software), and the last column shows the number of parameters
of the ANFIS to be tuned by the ASO-ANFIS. In this regard, the atoms (ASO algorithm) were
initiated in 405-dimensional space as the result of the Relief Attribute Evaluation method, 65
as the result of CF subclass evaluation, 445 as the result of Correlation Attribute Evaluation,
and 205 as the result of the GA. The search mechanism of the ASO iterated 200 times and
ended up with a validated RMSE (objective function), as shown in Table 5. The results in
Table 5 showed that the highest R2 (0.58) was produced by the ASO-ANFIS, with the selected

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Table 3. Classification accuracies.

Training data
Forest type Prod.Acc (%) User Acc (%)
ASO-ANFIS RF SVM ASO-ANFIS RF SVM
Natural Rhizophora forest 83.33 82.96 81.02 87.8 85.5 84.7
Natural mixed of Avicennia/Rhizophora forest 87.84 84.21 82.89 88.4 87.1 85.7
Natural regeneration of Avicennia forest 89.62 86.24 81.82 91.3 90.4 86.5
Rhizophora plantations forest 88.85 88.34 86.57 92.4 90.6 88.8
Avicennia plantations forest 88.54 85.57 80.00 91.4 89.2 86.0
other mangroves forest and shrubs 94.17 92.59 85.96 85.0 83.8 81.0
Overall Accuracy (%)
ASO-ANFIS RF SVM
89.18 87.57 85.37
Kappa Coefficient
ASO-ANFIS RF SVM
0.87 0.85 0.83
Validation data
Forest type Prod.Acc (%) User Acc (%)
ASO-ANFIS RF SVM ASO-ANFIS RF SVM
Natural Rhizophora forest 83.33 83.33 82.98 87.0 87.0 84.8
Natural mixed of Avicennia/Rhizophora forest 89.29 87.27 85.45 83.3 80.0 78.3
Natural regeneration of Avicennia forest 87.50 80.00 75.00 93.3 93.3 90.0
Rhizophora plantations forest 86.05 85.71 84.52 86.2 83.7 82.6
Avicennia plantations forest 78.95 73.68 68.42 88.2 82.4 76.5
other mangroves forest and shrubs 89.83 88.14 86.44 87.1 85.2 83.6
Overall Accuracy (%)
ASO-ANFIS RF SVM
86.76 84.70 82.58
Kappa Coefficient
ASO-ANFIS RF SVM
0.85 0.82 0.81
https://doi.org/10.1371/journal.pone.0233110.t003

features from the genetic algorithm, which outperformed the benchmarked methods, includ-
ing RF and SVR.
The feature combinations from Table 4 were used in the first layer of the ANFIS (Fig 2),
and the RMSE values from the use of different repressors are shown in Table 5. The results
showed a highest R2 value of 0.58, which was produced by the ASO-ANFIS with the selected
features from the genetic algorithm. This process resulted in ten features, including two from
the Sentinel-1A and eight spectral indices.The scatter plots are shown in Fig 5. The selection of
VV for AGB estimation also comes along with the works of [1,67], in which VV was found to
be sensitive to the increase in biomass and mangrove forest structure. The TSAVI was calcu-
lated using a soil adjustment factor [46]. WDRVI is an NDVI type, but NIR (this band was
used to measure WDRVI) was rescaled by a factor ranging from 0.1–0.5. This index increased
the linearity between the biomass and NIR, thus reducing the sensor saturation [68]. The con-
tribution of VH was reflected in the ratio between VH and VV, and the remaining indices,
which had been proven useful in previous studies [1,14], had a significant contribution to the
overall estimation.
Table 5 shows the estimation accuracies from the machine learning methods with different
combinations of features. With ten features from GA, the ASO-ANFIS generated the highest

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Fig 4. Mangrove forest classification with the use of the ASO-ANFIS. Background data were collected from https://
gadm.org/ and processed by the authors.
https://doi.org/10.1371/journal.pone.0233110.g004

R2 at 0.577 (rounded up to 0.58) and the smallest RMSE and MAE values of 70.88 and 55.458,
respectively, among other regression methods. The two ensemble algorithms were followed by
the regression methods of RF (RMSE = 82.227, R2 = 0.503) and RS (RMSE = 84.406, R2 =
0.484). SVR (R2 = 0.24) and MLP (R2 = 0.34) had the worst performance among the regression
methods. However, SVR (R2 = 0.33) and MLP (R2 = 0.40) performed better with 20 selected
features from the Relief Attribute Evaluation method. In addition, a test of statistical signifi-
cance was also implemented, as shown in Table 6, in which null hypothesis H0 was the equality
of performance. The p-values were all smaller than 0.05 (5%), so the differences were
significant.
The top three highest R2 values were from the ASO-ANFI, RS, and RF by using ten selected
features with the GA method, and the scatter plots between the predicted AGB and observed

Table 4. Selected features from different methods.

Feature selection No of selected Selected features Tunable parameters of
method features ANFIS�
Relief Attribute 20 ARVI, PNDVI, TSAVI, NDVI, OSAVI, SAVI, EVI, VH, ATSAVI, TVI, BWDRVI, RI, 405
Evaluation AVERAGEvhvv, GSAVI, GNDVI, TSARVI, CI, MULTvhvv, VV, I
CFs subclass evaluation 3 CI, GI, RATIOvvvh 65
Correlation Attribute 22 CI, RI, AVERAGEvhvv, VV, AVI, VH, WDVI, ATSAVI, SAVI, OSAVI, NDVI, GSAVI, GNDVI, 445
Evaluation WDRVI, CVI, PNDVI, ARVI, RATIOvvvh, EVI, RDVI, DIFFvvvh, BWDRVI
Generic Algorithm 10 CI, EVI, IF, RATIOvvvh, SIPI3, TSARVI, VIN, VV, WDRVI, WDVI 205
�
Tunable parameters of ANFIS are parameters of membership functions which are explained in Eq 1 and linear function in Eq 3.

https://doi.org/10.1371/journal.pone.0233110.t004

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Table 5. Statistical indicators from machine learning models by using the validation dataset.
Feature selection method No of features SVR MLP RF RS ASO-ANFIS
RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2 RMSE MAE R2
Relief Attribute Evaluation 20 99.31 74.50 0.33 120.23 94.76 0.40 84.41 63.77 0.48 85.55 65.49 0.45 76.37 59.93 0.51
CFs subclass evaluation 3 101.19 75.85 0.28 111.25 89.63 0.28 95.44 73.61 0.43 92.16 70.66 0.46 89.67 69.12 0.47
Correlation Attribute Evaluation 22 164.89 87.44 0.15 147.03 99.21 0.26 86.04 66.56 0.46 86.60 66.23 0.44 75.95 60.41 0.52
Generic Algorithm 10 120.05 81.70 0.24 127.21 94.37 0.34 82.23 62.53 0.50 84.41 65.72 0.48 70.88 55.46 0.58
https://doi.org/10.1371/journal.pone.0233110.t005

Fig 5. Scatter plots of 10 selected features by GA against the AGB of the sample plots: a) CI, b) EVI, c) IF, d) VH/VV ratio, e) SIPI3, f) TSARVI, g) VIN, h) VV, i)
WDRVI, and k) WDVI.
https://doi.org/10.1371/journal.pone.0233110.g005

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Table 6. Significance test between the ASO-ANFIS and the benchmarked classifiers.
SVR MLP RF RS
ASO-ANFIS V = 68 V = 68 V = 68 V = 68
p-value = 0.021 p-value = 0.021 p-value = 0.021 p-value = 0.021
https://doi.org/10.1371/journal.pone.0233110.t006

AGB are shown in Fig 6. As shown in Fig 6(C), the RS underestimated the AGB for the entire
validation dataset. It can also be seen that for values lower than 100 Mg ha-1, the ASO-ANFIS
and RF were likely to overestimate the AGB, and after this value, the two methods seemed to
underestimate the AGB. This situation occurred due to the saturation level of the C-bands,
and the spectral reflectance can only partly offset the saturation effect.
From the map (Fig 7), the AGB was grouped into the five following classes: (i) less than
50 Mg ha-1, (ii) from 50 to 100 Mg ha-1, (iii) from 100 to 150 Mg ha-1, (iv) from 150 to 200 Mg
ha-1, and (v) over 200 Mg ha-1. With AGB values between 56.72 and 339.85 Mg ha-1 (aver-
age = 125.63 Mg ha-1), the predicted spatial pattern of the AGB value was consistent with

Fig 6. Scatter plots between the predicted AGB and observed AGB by using ten selected features from GA. a) validation dataset, b) training dataset, and c) estimated
AGB in ascending order of observed AGB.
https://doi.org/10.1371/journal.pone.0233110.g006

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Fig 7. Mangrove aboveground biomass map of the study area. Background spatial data were collected from https://
gadm.org/ and processed by the authors.
https://doi.org/10.1371/journal.pone.0233110.g007

actual observations [2,68]. The non-mangrove forested areas, such as bare land and agricul-
tural land, were removed from the result of the AGB map.
As shown in Fig 7 and Table 7, the area with an AGB over 200 Mg ha-1 was approximately
16,066.84 ha (approximately 23.31% of the mangrove forests of the study area), which was
mainly distributed in the Rhizophora plantation forest (8,277.09 ha), natural Rhizophora forest
(3,217.85 ha), natural mixed Avicennia/Rhizophora forest (2,712.74 ha), and other mangroves
forest and shrubs (1,032.42 ha). Approximately 12,020.95 ha within the AGB value of 150–200
Mg ha-1 was found in the Rhizophora plantation forest (6,960.27 ha), other mangrove forests,
shrubs (3,326.72 ha), and natural mixed Avicennia/Rhizophora forest (1,044.14 ha). The larg-
est areas of moderate and low AGB (smaller than 150 Mg ha-1) were predominantly observed
in the Rhizophora plantation forests and other mangrove forests and shrubs.

Table 7. The statistical results of the types of mangrove areas were classified into five ranges of aboveground biomass estimates.
The area with mangrove forest density of Total areas (ha)
0–50 50–100 100–150 150–200 >200
Mg ha-1 Mg ha-1 Mg ha-1 Mg ha-1 Mg ha-1
Natural Rhizophora forest 9.42 6.50 51.83 150.83 3,217.85 3,436.42
Natural mixed of Avicennia/Rhizophora forest 89.72 22.01 454.53 1,044.14 2,712.74 4,323.15
Natural regeneration of Avicennia forest 42.79 4.64 68.87 366.66 543.24 1,026.21
Rhizophora plantations forest 1,238.72 1,177.03 8,809.93 6,960.27 8,277.09 26,463.05
Avicennia plantations forest 19.58 21.65 52.71 172.32 283.50 549.77
other mangroves forest and shrubs 6,078.27 9,612.18 13,071.82 3,326.72 1,032.42 33,121.39
Total areas (ha) 7,478.51 10,844.01 22,509.69 12,020.95 16,066.84 68,920.00
https://doi.org/10.1371/journal.pone.0233110.t007

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

The spatial distribution of the mangrove forest was observed in two regions: (i) in the west-
ern-southwestern area, this was the new alluvial land with low and flat terrain and a flooding
depth of over 80 cm and (ii) the eastern coastal area of the Ngoc Hien, Nam Can and Don Doi
districts. The mangrove forests here grow on acidic and highly compacted soil due to the ero-
sion impacts from the influence of coastal currents and waves. The area was mainly Rhizo-
phora plantation forest and mixed Avicennia/Rhizophora forest. Unlike in the western-
southwestern area, in the eastern coastal area, the area of natural mangrove forests was in very
low land, and there was no natural Avicennia forest (pioneering forest species encroaching on
the sea).
Mapping AGB is a major concern at the global scale and for many developing countries as
it is a challenging task because of the lack of field data [69]. For a given ecosystem, these maps
can be used for forest monitoring, deforestation, forest degradation, and other forest-related
industries, such as conservation, sustainable management, and increased carbon storage [70].
Carbon accumulation in mangrove forests is influenced by tree density, tree species, tree age,
organic decomposition in soil, and regular submergence tides. Frequent tidal inundation and
the degree of organic decomposition in the anaerobic environment are key factors enabling
the mangrove forest in Ca Mau to become a greenhouse-gas reservoir. Therefore, the protec-
tion of the vast carbon storage in mangrove forests and on the peatlands in Ca Mau, Vietnam
and throughout Asia, in general, is crucial to prevent the release of carbon dioxide and meth-
ane into the atmosphere.

Search strategy of the ASO

The hybrid model outperformed the benchmarked regression methods by comparing three
common indicators, as shown in Table 5. This result was achieved through the robust search
mechanism of the ASO in tuning parameters of the ANFIS. Indeed, the parameters of the ASO
also influenced how the search operated. Fiver clusters were determined in the ANFIS and two
parameters in the ASO were defined through the trial and error process. Fig 8(A) shows the
RMSE variation curve after 2000 iterations. The exploration search can be realized by the sud-
den jumps in the graph (almost vertical lines in the curve) with large variation at the beginning
stages.

Fig 8. The performance of the ASO. a) Variation in the RMSE values of the validation plots with the use of the ASO and (b) variations in RMSE by the number of
features using the ASO.
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PLOS ONE Mangrove forest classification and aboveground biomass estimation

Fig 8(B) shows another aspect of the ASO operation in combination with feature selection
by GA. A feature selection solution was represented by a 1 x 42 dimension vector with binary
values of 1 or 0, in which the selected features were represented by 1 and vice versa. For each
iteration step in the GA, the ASO-ANFIS was triggered to search for the best RMSE. The hori-
zontal axis represents the number of features that were selected during the search, regardless
of how the subsets were determined. The y-axis shows the best RMSE value among the values
that were generated by the ASO-ANFIS model.

Potential uses of C bands for mangrove AGB estimation

There have been a large number of studies on the application of the L-band in AGB estimation
that have investigated the relationship of backscatter signals, their transformed ratios, and the
field estimation of AGB. The penetration capability of the L-band makes it useful for measur-
ing tree cover, canopy height, and, consequently, in estimating AGB because of the correlation
among these factors. The C-band has several drawbacks in comparison to the L-band in AGB
applications because of its limited ability to penetrate forest canopies. The dependency of AGB
estimation on the SAR wavelength has been reported in numerous studies, which have focused
on the discussion of the saturation level, in which the saturation level of the C-band is typically
low in AGB estimation (50–70 Mg ha-1) and the level of the L-band is approximately 100–150
Mg ha-1 [1,15]. In this study, the AGB mean of the sample plots was higher than the saturation
level of the Sentinel-1A dataset, which might result in underestimation in the high AGB area.
However, this situation can partly be minimized with the contribution of the optical data to
the AGB estimation, as multispectral reflectance offsets the saturation effect [12,14]. The study
of [71] successfully sampled plots with values higher than 300 Mg ha-1 by using SPOT-6 or the
combination of Sentinel-1 and Sentinel-2 imagery to produce reliable AGB of approximately
200 Mg ha-1 [30]. The NIR, red and green bands play a key role among optical data for AGB
estimation [14] because of their strong interaction with trees, and they were the main compo-
nents for the measurements of the vegetation-derived features as described in Table 4. There
have been limited studies on the combination of Sentinel-1A and SPOT-6-derived vegetation
indices, and machine learning methods for AGB estimation and the uses of such datasets were
the main objective of this study.
From the experiment, it could be noticed that the combination of vegetation indices from
multispectral imageries with SAR improved the accuracy of AGB estimation, even though
there was still underestimation (in plot samples of having AGB higher than around 120Mg ha-
1) and overestimation (in plot samples of having AGB smaller than around 120Mg ha-1) of the
AGB (Fig 6C).
This paper presented a novel model for advancing atom search optimization in searching
for the parameters of the ANFIS by using 158 sample plots. The highest R2 (0.577) in this study
was satisfactory compared to the (R2 = 0.28–0.44) in [72], (R2 = 0.596) in [1] with L-band PAL-
SAR, (R2 = 0.46) in [3], even though the C-band was not as good as the L-band because of its
limited ability to penetrate the canopy. Then, the proposed model (ASO-ANFIS) with ten
selected features was used to generate the AGB map along the coastal area of Ca Mau Province
(Fig 7).

Conclusions and further remarks

Ca Mau Province is the largest reserved area for the regrowth and expansion of mangrove for-
ests in Vietnam. The estimation results from the proposed methods showed a spatial variation
in biomass that ranged from less than 40 Mg ha-1 to 339.85 Mg ha-1. This result was consistent
with the in situ plot samples, with a considerable correlation between the estimated values and

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

observed values. As the AGB is an important estimation indicator in sustainable forest man-
agement, a timely estimation of it is crucial to monitor the surface changes or potential loss
and degradation of the area’s mangrove ecosystems.
The ASO significantly improved the performance of the ANFIS regression through com-
parison with benchmarked functions by using common statistical indicators with different
combinations of features. The best values were found at RMSE = 70.882, MAE = 55.458, and
R2 = 0.577. Another essential concept is that feature selection played an essential role in defin-
ing the most critical predictor variables before running any regression methods. This study
investigated the potential uses of both optical and radar datasets, and it was found that the
combination of both types of data is crucial in eliminating the saturation effect and in improv-
ing the estimation accuracy. The backscatter information from radar data and vegetation indi-
ces were evaluated to determine how they drove the changes in tree structures and associated
AGB. The optimal selections are subject to the frequency of the radar dataset (X, L, P, or C
bands) and spectral information of the optical data (multiple spectral bands).
This paper investigates artificial intelligence as machine learning methods and has become
a trending topic because of its broad applications in almost all research fields. The increase in
computational capacity and multiple sensor platforms have made geolocated data overwhelm-
ingly available for spatial analysis. For the understanding of carbon flux, machine learning is
predominantly applied to the methods used in the regression of spectral reflectance and back-
scatter of remotely sensed satellite imagery to the in situ measurements of AGB. This paper
aimed to investigate novel hybrid machine learning algorithms for data fusion and spatial and
temporal modeling for biomass estimation in the coastal area of Ca Mau Province in Vietnam.
The findings are practically relevant, and the methodology is scientifically sound. This research
is a novel approach and contributes to global knowledge in the field of forest cover estimation.

Acknowledgments
The authors thank all anonymous reviewers for their critical and constructive comments,
which have improved the quality of the manuscript.

Author Contributions
Conceptualization: Minh Hai Pham, Van-Manh Pham, Quang-Thanh Bui.
Data curation: Minh Hai Pham, Van-Manh Pham, Quang-Thanh Bui.
Formal analysis: Minh Hai Pham, Van-Manh Pham, Quang-Thanh Bui.
Funding acquisition: Minh Hai Pham, Thi Hoai Do.
Investigation: Minh Hai Pham, Van-Manh Pham, Quang-Thanh Bui.
Methodology: Minh Hai Pham, Van-Manh Pham, Quang-Thanh Bui.
Project administration: Minh Hai Pham, Thi Hoai Do.
Supervision: Quang-Thanh Bui.
Validation: Minh Hai Pham, Van-Manh Pham, Quang-Thanh Bui.
Visualization: Minh Hai Pham, Quang-Thanh Bui.
Writing – original draft: Minh Hai Pham, Van-Manh Pham, Quang-Thanh Bui.
Writing – review & editing: Minh Hai Pham, Thi Hoai Do, Van-Manh Pham, Quang-Thanh
Bui.

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PLOS ONE Mangrove forest classification and aboveground biomass estimation

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