A Review of Spectral Indices for Mangrove Remote Sensing

Abstract

Mangrove ecosystems provide critical goods and ecosystem services to coastal communities and contribute to climate change mitigation. Over four decades, remote sensing has proved its usefulness in monitoring mangrove ecosystems on a broad scale, over time, and at a lower cost than field observation. The increasing use of spectral indices has led to an expansion of the geographical context of mangrove studies from local-scale studies to intercontinental and global analyses over the past 20 years. In remote sensing, numerous spectral indices derived from multiple spectral bands of remotely sensed data have been developed and used for multiple studies on mangroves. In this paper, we review the range of spectral indices produced and utilised in mangrove remote sensing between 1996 and 2021. Our findings reveal that spectral indices have been used for a variety of mangrove aspects but excluded identification of mangrove species. The included aspects are mangrove extent, distribution, mangrove above ground parameters (e.g., carbon density, biomass, canopy height, and estimations of LAI), and changes to the aforementioned aspects over time. Normalised Difference Vegetation Index (NDVI) was found to be the most widely applied index in mangroves, used in 82% of the studies reviewed, followed by the Enhanced Vegetation Index (EVI) used in 28% of the studies. Development and application of potential indices for mangrove cover characterisation has increased (currently 6 indices are published), but NDVI remains the most popular index for mangrove remote sensing. Ultimately, we identify the limitations and gaps of current studies and suggest some future directions under the topic of spectral index application in connection to time series imagery and the fusion of optical sensors for mangrove studies in the digital era.

1. Introduction

Mangrove is a term which corresponds to intertidal ecosystems or lignified plant communities that grow in coastal environments between 40°S and 30°N throughout the world (Figure 1). The mangrove boundary is extended to the south of Japan (30.4°N) and Bermuda (32.4°N); to the south of New Zealand (38.05°S), Australia (38.85°S), and the east coast of South Africa (32.98°S) [1,2]. Mangrove distribution is restricted generally to areas where the mean temperature ranges 20–35°C, annual rainfall is between 1500–2500 mm, and there is a substantial riverine input of freshwater discharge [2]. Actually, the number of frozen days in the year may play on mangrove presence at high latitudes [3]. Decreases in the frequency of extreme cold occurrences could lead to considerable increases in mangrove cover near the current poleward limits of mangrove forests. The global mangrove distribution is classified into two groups, including the Indo-West Pacific (IWP) and the Atlantic East Pacific (AEP). Mangroves initially developed on the Tethys Sea’s coastlines in the late Cretaceous-early Tertiary period [1,2,4]. Three million years ago, modern mangrove taxa emerged on the eastern borders of Tethys, diversified into present-day IWP regions, and subsequently spread into AEP regions [5,6]. The richness in the distribution of mangrove species reduces from the IWP to AEP. Globally, there are approximately 77 mangrove species, but about 54 species in 20 genera from 16 families constitute the group of “true mangroves” occurring only in mangrove habitats. Among these 77 species, 65 species in 32 genera and 24 families are recorded from IWP, while only 15 species in 10 genera and 8 families are in AEP [1,2]. The most commonly found genera in both IWP and AEP are Rhizophora and Avicennia [2].

The biophysical variables of mangroves (i.e., leaf area, basal area, tree height, percent canopy closure, diameter at breast height, carbon stock, and biomass) mostly depend on climatic conditions, while sea level rise has an influence on the structure and spatial distribution of mangroves [7,8]. Temperature, precipitation, and storminess explain 74% of the global trends in the maximum values of canopy height and above-ground biomass [7]. Globally, 75% of mangroves are distributed in tropical regions. The largest cover and highest mangrove diversity are found in Asia (39%), followed by Africa (21%, but mostly on the eastern side), North and Central America (15%), South America (12.6%) and Oceania (Australia, Papua New Guinea, New Zealand, south Pacific islands) (12.4%) [9,10]. The mangroves in the equatorial regions have the maximum biomass and tree canopies can reach an average height of 30–40 m. The highest mangrove forests are found in Gabon, an equatorial African nation, where heights reach 62.8 m. A quarter of the estimated 11.7 Pg C global mangrove carbon stock, which includes soil, above- and below-ground biomass, is stored in Indonesia [7,8]. These biophysical parameters gradually decrease with increasing latitude due to varied temperature and environmental conditions.

Mangrove ecosystems produce valuable goods and services, including regulation (e.g., coastal protection, water filtration), supply (e.g., fisheries, aquaculture, timber, fuel, honey, construction materials, medicine), culture (e.g., tourism), and support (e.g., nursery habitats, climate mitigation) [13,14]. In some areas, mangroves have been proposed to provide a natural barrier to coastal erosion process, defending inland areas home to 120 million people from natural hazards (e.g., typhoon, cyclones, tsunamis) [7,15]. Mangrove restoration for coastal defence is expected to be up to five times more cost-effective than “grey infrastructure” such as breakwaters [16]. Mangroves also regulate water quality, and it is estimated that 2–5 ha of mangroves can treat the effluent from 1 ha of some aquaculture practices [17]. The carbon storage potential of mangroves is 3–5 times higher than that of tropical upland forests due to strong carbon storage in the soil [18,19]. Mangroves are also a valued source of timber, fuel, and tourism. There are over 2000 mangrove related attractions globally, such as boat tours, boardwalks, kayaking, and fishing [20]. Together, the economic value from mangrove ecosystem services has been estimated to exceed 800 billion per year [15,21]. However, approximately 35% of the global mangrove forests has been lost over the past 50 years due to both anthropogenic activities and physical stressors [22,23]. While restoration of mangrove forests has been increasing over the past 40 years, the net reduction in mangrove cover area and species richness is still a high 1–2% per year [22,24].

Traditionally, monitoring mangrove ecosystems used field observation and survey methods [25,26,27]. However, these approaches are difficult to monitor and measure mangroves in situ due to their dense understory and geographical location in intertidal zones [21,25]. Additionally, field observation and survey methods are labour-intensive, costly, and frequently limited in extent. Many surveys are qualitative and difficult to reproduce or revisit over time. Remote sensing (RS) has overcome the drawbacks of traditional field surveys and is has been continuously improving in terms of spatial resolution, revisit time and user costs over the past four decades [25,28]. RS is acknowledged in this context as the science and technology of acquisition of information about Earth’s surface materials from a distance, typically from aircrafts or satellites [29]. The two types of remote sensing we refer to are (i) optical and (ii) radar sensors, which are classified according to the energy source of the signal used to identify the object. The remotely sensed data, acquired from these sensors, allows us to gather accurate information about the geographical distribution of mangrove ecosystems and biophysical properties at the pixel level [13,27].

In remote sensing, mangroves can be identified based on the textural and spectral properties of the canopy and leaves [13,30]. Their structural appearance, which can be either homogenous across the forest or heterogeneous, is affected by factors including species composition, growth form, density, and stand height. Almost all mangrove species can be discriminated within the visible and near infrared (NIR) region because of scattering in the spongy mesophyll cells in plants [31,32]. Using structural information extracted from several remotely sensed products regional and global estimation can be made of mangrove height, canopy, species succession, biomass, and carbon stocks [26]. The highest spectral reflectance of mangroves was observed in the NIR region for both Landsat 8 and Sentinel-2A surface reflectance sources (Figure 2). With the Sentinel-2A in particular, mangrove reflectance was observed to rise rapidly at the red-edge. Therefore, mangrove ecosystems can be observed using indices computed from spectral bands in the visible and NIR regions of optical remote sensing.

The importance of remote sensing in mangrove studies has been recognised in many review studies [25,26,28,30,33,34,35]. These publications serve as a good starting point for researchers who are interested in mangrove remote sensing. However, the application of different spectral indices in mangroves has not been reviewed extensively in most of these studies. For instance, Green et al. (1998) [33] considered the significance of remote sensing for mangrove mapping from 1972 to 1996. While the study is the first paper that mentioned applying NDVI to mangrove classification, it only focused on NDVI even though at the time there were over 40 vegetation indices that could have been relevant to mangrove ecosystems. During the 1998–2018 period, most review papers highlighted remote sensing as a technique or approach for mangrove studies, while remote sensing has been defined as the science of acquiring information from distance [29,36,37]. Recently, Wang et al. (2019) [25] revealed common gaps in previous publications (i.e., research topics, key milestones, and mangrove driving forces) in mangrove remote sensing and investigated the importance of remote sensing for mangrove studies from 1956 to 2018. However, Wang et al. (2019) did not clearly state what kinds of spectral indices are specifically applied to mangrove remote sensing.

The present study intends to address the aforementioned knowledge gaps, by answering the following research question: what spectral indices have been applied and have been proven effective for mangrove remote sensing? Our objectives are to (i) examine and categorise spectral indices used in publications related to mangrove remote sensing; (ii) assess their applications in the study of mangrove ecosystems; and (iii) propose future directions for the application of additional spectral indices in mangrove remote sensing.

2. Search Strategy and Data Analysis

Mangrove scholars used various qualitative and quantitative approaches to understand and organise earlier findings of mangrove studies. Among these, a quantitative analysis of academic literature, defined as bibliometrics, was investigated as a potential tool to introduce a systematic, transparent, and reproducible review process [38,39,40,41]. Compared to other literature review techniques, bibliometric analysis of the published literature is effective to identify research gaps and direct future avenues of research [41]. For bibliographic citations, Web of Science (WoS) and the Scopus platforms are the most extensive databases which are widely used to obtain metadata for bibliometric analysis [42,43]. Scopus was launched in 2004, but WoS launched in 1997 and is considered the earliest international bibliographic database [42,44]. WoS comprises four citation databases with more than 10,000 journals [45]. Journals indexed in the WoS must meet 28 criteria (i.e., 24 quality criteria and 4 impact criteria) [44,46]. The fulfilment of 28 criteria contributes to enhancing academic quality and minimising the influence of multiple predatory journals. The journal listed in the WoS database primarily provides impact factor (a ratio between citations and citable items published the previous year) and h-index (an index based on a list of publications ranked in descending order by Times Cited count) [46]. Therefore, journals with high impact factor or h-index are cited more often than journals with lower impact factor or h-index.

Various keywords were entered in the searching process associated with global mangrove ecosystems based on spectral indices application (

Table 1. The predefined keywords of the searching process.

No.	Keywords
1	vegetation index and mangrove
2	comprehensive mangrove quality index
3	a mangrove recognition index
4	mangrove and an index analysis approach
5	leaf area index and mangrove
6	spectral mangrove index

). We used Thomson Data Analyzer (TDA) integrated in the Web of Science (WoS) Core Collection to retrieve annual publications and their citations [47]. The keyword search resulted in 293 papers published between 1992 and 2021. We then reviewed the abstract and content of the 293 papers and removed from our study the papers that did not relate to the application of spectral indices in mangroves. This left 195 publications (including 90 journal papers, 14 conference papers and one book chapter) for our review.

Full records (i.e., author, title, source, abstract) and cited references of the search results were downloaded in the BibTex format in several batches, each comprising no more than 500 data entries. For further processing, all of the obtained result files were zipped together and imported into the R-statistical and VOSviewer software packages. The bibliometric analysis was carried out with the help of Bibliometrix package in R [38]. The annual cited times of the gathered articles were calculated using TDA’s citation report tool, while the top journal sources for publication and citation were determined using Bibliometrix package. Finally, using VOSViewer software [48], we performed co-word analysis to visualise density networks of author keywords for trend analysis in mangrove remote sensing. The co-word method is a technique to analyse the co-occurrences of key words and identify relationships and interactions between the topics researched and emerging research trends [48]. Details regarding the theory and practical function of the co-word approach utilising VOSViewer software may be found at [45,48]. However, the record of the online bibliographic database prior to 1990 may be incomplete [42,44,49] because the internet-based Web of Science was firstly launched in 1997. Therefore, several publications from print only sources may be missing. We thus supplemented our database with metadata from outcomes of Green et al. (1998) [33] and Bannari et al. (1995) [50] to increase the number of studies applying spectral indices in mangrove studies prior to 1996.

Publications and annual citations in this field of research, retrieved from citation report analytics of the Web of Science Core Collection, significantly increased between 1996 and 2021 (Figure 3). The number of publications dropped in 2021, which may be related to COVID-19 pandemic because most field trips were delayed or cancelled and this impacted field data collection for validation and other known impacts on academic workloads [51]. Most of the publications (21) were published in the journal of Remote Sensing (IF: 4.848, Open access), others were mainly from the International Journal of Applied Earth Observation and Geoinformation (8, IF: 5.993, Open access), International Journal of Remote Sensing (8, IF: 3.362), Estuarine, Coastal and Shelf Science (7, IF: 2.904), Modeling Earth Systems and Environment (6, IF: N/A) and ISPRS Journal of Photogrammetry and Remote Sensing (5, IF: 10.565). The journals Ecological Indicators, Regional Studies in Marine Science, and Wetlands had four papers each. The remaining journals had published less than three papers involving mangrove remote sensing since 1996. In-text citations of work that applied mangrove remote sensing indices were found in Remote Sensing of Environment (881, h-index: 281), Remote sensing (445, h-index: 124), International Journal of Remote Sensing (437, h-index: 174), Estuarine, Coastal and Shelf Science (249, h-index: 134), Aquatic Botany (170, h-index: 94). The publications most cited were those in higher ranked (higher IF) journals, regardless of the open access status of the journal.

3. Overview of Spectral Indices Used in Mangrove Remote Sensing

A spectral index is an equation that combines pixel values from two or more spectral bands in a multispectral image using various algorithms, mainly focused on band ratio or feature scaling (e.g., normalised or standardised algorithms) [52,53]. Spectral indices are calculated to highlight pixels in an image that not only show the relative abundance of a land cover of interest, but also emphasise an ecosystem function [52,53,54]. They show better sensitivity than individual spectral bands for spectral signature detection. Throughout the mission of Earth surface observation, spectral indices have significantly contributed a more thorough understanding of environments and ecosystems across space and time [28]. The geographical extent of mangrove studies using spectral indices has also seen a significant change over time (

Table 2. The mangrove study area of spectral indices application, obtained from our searched publications.

Country/Region	Before 2000s	2001–2015	2016–2021	%
US	2 (33%)	6 (15%)	11 (7%)	9.7
Mexico	1 (17%)	6 (15%)	9 (6%)	8.2
India	0	6 (15%)	19 (13%)	12.8
China	0	3 (8%)	21(14%)	12.3
Australia	0	1 (3%)	6 (4%)	3.5
Malaysia	0	5 (13%)	7 (5%)	6.2
Indonesia	0	1 (3%)	18 (12%)	9.7
Vietnam	0	1 (3%)	7 (5%)	4.1
Others	3 (50%)	7 (18%)	29 (19%)	20
Intercontinental regions and globe	0	3 (8%)	25 (16%)	13.5

). Most studies (92%) were carried out at the national level, while a few publications implemented research on intercontinental/global scale during the 2000–2015 period and this trend is increasing (16% in 2021 vs. 8% in 2016). The study areas were mainly in India (12.8%), Mainland China (12.3%), Indonesia (9.7%), the US (9.7%), and Mexico (8.2%).

Spectral indices can be categorised as either satellite or airborne system indices based on the platforms used for data acquisition. Depending on the spectral bands of passive satellite remote sensing, spectral indices may be further grouped into indices with (i) simple ratio, (ii) visible and near-infrared (VNIR) bands, (iii) visible and red edge bands, (iv) visible and mid-infrared bands, and (v) visible and shortwave infrared (SWIR) bands. In addition, following the applications of spectral indices in mangrove remote sensing, we separate spectral vegetation indices and spectral mangrove-specific indices. The key differences between the two types of indices are their applications and the spectral bands used in the indices. Vegetation indices are spectral indices computed using spectral bands in the visible, red edge, and near-infrared regions [55]. They have been widely applied to mangrove ecosystems previously [13,30]. However, as mangroves have common spectral characteristics as other vegetation, separating mangroves from other types of vegetation using a single vegetation index is challenging [56]. To address this issue, some mangrove-specific spectral indices have been proposed for separating mangroves from terrestrial vegetation [56,57,58,59,60]. These spectral mangrove indices include spectral bands from VNIR to SWIR regions. In this review, based on publications that used spectral indices for mangrove ecosystems and the approaches they applied, we classified indices into four categories: (i) visible and near-infrared bands; (ii) visible and red edge bands; (iii) visible bands of airborne systems; and (iv) spectral mangrove specific- indices.

3.1. Spectral Indices with Visible and Near-Infrared Bands (VNIR)

Indices measured from spectral bands in the VNIR regions are acknowledged as vegetation indices [50], except Normalized Difference Water Index (NDWI) [61]. The history of spectral vegetation indices development is associated with the Landsat mission in 1972. Particularly, Pearson and Miller (1972) developed the first vegetation indices, i.e., Ratio Vegetation Index (RVI) and Vegetation Index Number (VIN), to estimate and monitor vegetative cover [50,62]. Following Pearson and Miller (1972), Rouse et al. (1973) introduced the Normalized Difference Vegetation Index (NDVI) that is now widely applied for land cover and environmental studies [63,64]. Over the last four decades, more than 40 vegetation indices have been developed, but 28 of these were used in mangrove investigations (

Table 3. The 28 indices in the spectral wavelength 400–1000 nm used in mangrove research (R: Red; G: Green; B: Blue; and NIR: Near Infrared). For whole algorithms, L is a soil adjustment factor that addresses nonlinear model, differential NIR and red radiant transfer through a canopy [72]; a and b are soil line factors [73]; c₁ and c₂ are coefficients of atmosphere resistance term which use blue band to correct for aerosol influences in red band [69].

No.	Spectral Index	Formula	Reference
1	Vegetation Index Number [62]	$VIN=\frac{R}{NIR}$	[74,75]
2	Ratio Vegetation Index [62]	$RVI=\frac{NIR}{R}$	[58,59,65,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91]
3	Normalized Difference Vegetation Index [63]	$NDVI=\frac{NIR-R}{NIR+R}$	From 1991 to 2011: [65,76,77,78,92,93,94,95,96,97,98,99,100,101,102,103,104,105] Since 2012: [58,59,60,74,75,80,81,82,83,84,85,86,87,89,90,91,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209]
4	Enhanced Vegetation Index [69]	$EVI=\frac{2.5\left(NIR-R\right)}{NIR+c_{1}R-c_{2}B+L}$	[3,59,74,75,80,82,84,90,123,144,146,149,153,160,169,172,176,186,191,196,197,201,206,207,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225]
5	Perpendicular Vegetation Index [73]	$PVI=\frac{NIR-aR-b}{\sqrt{a^2+1}}$	[76,138]
6	Normalized Green-red Difference Index [226]	$NGRDI=\frac{G-R}{G+R}+0.08$	[227]
7	Difference Vegetation Index [228]	$DVI=NIR-R$	[74,75,76,84,97]
8	Soil Adjusted Vegetation Index [67]	$SAVI=\left(1+L\right)\frac{NIR-R}{NIR+R+L}$	[58,76,83,84,85,89,90,91,108,110,120,123,132,138,139,145,146,150,169,170,201,207,225]
9	Transformed soil adjusted vegetation index [68]	$TSAVI=\frac{a(NIR-aR-b)}{R+aNIR-ab}$	[76,196]
10	Soil adjusted ratio vegetation index 2 [229]	$SAV{I}_2=\frac{NIR}{R+a÷b}$	[76]
11	Global environment monitoring index [230]	$GEMI=n(1-0.25n)-\frac{R-0.125}{1-R}$ at $n=\frac{2(NI{R}^2-R^2)+1.5NIR+0.5R}{NIR+R+0.5}$	[74,75,84,109,139,196]
12	Atmospherically Resistant Vegetation Index [231]	$ARVI=\frac{NIR-2R+B}{NIR+2R-B}$	[74,84,196,232]
13	Non-linear vegetation index [233]	$NLI=\frac{NI{R}^2-R}{NI{R}^2+R}$	[83]
14	Modified soil-adjusted vegetation index [234]	$MSAVI=\frac{2NIR+1-\sqrt{{(2NIR+1)}^2-8(NIR-R)}}{2}$	[81,85,86,108,132,145,169,191,207]
15	Renormalized difference vegetation index [235]	$RDVI=\frac{NIR-R}{\sqrt{NIR+R}}$	[81,85,108]
16	Modified Simple Ratio [236]	$MSR=\frac{NIR/R-1}{\sqrt{NIR/R}+1}$	[85,145]
17	Green Normalized Difference Vegetation Index [237]	$GARI=\frac{NIR-\left[G-1.7\ast (B-R)\right]}{NIR+\left[G-1.7\ast (B-R)\right]}$	[130,196]
18	Optimized Soil Adjusted Vegetation Index [238]	$OSAVI=\frac{NIR-R}{NIR+R+0.16}$	[82,89,109,110,115,130,177,199]
19	Green Normalized Difference Vegetation Index [239]	$GNDVI=\frac{NIR-G}{NIR+G}$	[81,85,87,89,119,139,145,193,207]
20	Green Leaf Index [240]	$GLI=\frac{(G-R)+(G-B)}{(G+R)+(G+B)}+0.07$	[227]
21	Triangular Vegetation Index [241]	$TVI=0.5\times [120(NIR-G)-200(R-G)]$	[74,82,84,85,108,207]
22	Transformed Difference Vegetation Index [242]	$TDVI=1.5\left[\frac{NIR-R}{\sqrt{NI{R}^2+R+0.5}}\right]$	[109,115]
23	Modified Non-Linear Vegetation Index [243]	$MNLI=\frac{1.5(NI{R}^2-R)}{NI{R}^2+R+0.5}$	[83]
24	Modified Chlorophyll Absorption Ratio Index 1 [244]	$MCAR{I}_1=1.2\times [2.5(NIR-R)-1.3(NIR-G)]$	[85,89,199,232]
25	Modified Chlorophyll Absorption Ratio Index 2 [244]	$MCAR{I}_2=MTV{I}_2=\frac{1.5\left[1.2\left(NIR-G\right)-2.5\left(R-G\right)\right]}{\sqrt{{\left(2NIR+1\right)}^2-\left(6NIR-5\sqrt{R}\right)-0.5}}$
26	Modified Triangular Vegetation Index [244]
27	Chlorophyll Vegetation Index [245]	$CVI=\frac{NIR\ast R}{G^2}$	[119,145,196]
28	Enhanced Vegetation Index 2 [71]	$EV{I}_2=2.5\left(\frac{NIR-R}{NIR+2.4R+L}\right)$	[59,74,75,84,89,132,193,196,207,246]

). Two categories of vegetation indices can be separated: ones that include only spectral bands and others that include spectral bands that are adjusted by non-spectral factors [e.g., soil adjustment factor (L), soil line factors (a, b), and coefficients of atmosphere resistance (c₁ and c₂)]. The first category (e.g., RVI, VIN, NDVI–Table 3) is based on linear combinations (difference or sum) of spectral bands or raw band ratios without considering environmental interactions. The second group (e.g., SAVI, TSAVI, EVI–Table 3) is based on the knowledge of physical phenomena which explains interactions between electromagnetic radiation, the atmosphere, the vegetative cover, and the soil background.

In the first group without adjustment factors, NDVI is the first index to show the highest correlation with field measured mangrove canopy cover (r = 0.91), higher than PVI, GVI, and RVI [65]. NDVI (No.3–Table 3) is a simple indicator that is acquired in red (visible) and near-infrared (NIR) regions, based on a normalised algorithm. The NIR band in the NDVI equation is useful for vegetation detection because healthy vegetation (which contains chlorophyll) reflects more NIR compared to other wavelengths [63]. The normalised algorithm mitigates the atmospheric effects and the impacts of sensor calibration degradation in the red and NIR bands [64,66]. Mathematically, NDVI also forms the basis for other indices. For example, a bijective relationship between NDVI and VIN is demonstrated by Equation (1). However, the NDVI values are affected by soil background when the green leaf area is small or the majority of the scene is soil [67,68]. Therefore, a number of vegetation indices in the second category have been developed for taking into account environmental conditions. For example, to adjust soil background, soil factors (e.g., L, a, and b) were included in SAVI and TSAVI (No.8–9, Table 3). Both indices SAVI and TSAVI are equal to NDVI, if the value of the soil adjustment factor in SAVI (No. 8, Table 3) is zero (L = 0) or the slope (a) and ordinate (b) at the origin of bare soil line parameters in TSAVI (No.9, Table 3) are one and zero (a = 1 and b = 0), respectively.

$$NDVI=\frac{NIR-R}{NIR+R}=\frac{NIR/R-1}{NIR/R+1}=\frac{VIN-1}{VIN+1}$$

(1)

Some studies show that the indices that include adjustment factors are less sensitive to atmospheric and soil background effects than the first-generation spectral indices [50,64,65]. Enhanced Vegetation Index (EVI, No.4–Table 3) [69], for example, which corrects for some atmospheric conditions and canopy background noise, and provides a better estimation of mangrove biophysical properties in high density forests than does NDVI [70]. However, the blue band requirement may be a disadvantage of EVI, which cannot be generated from optical sensors that do not have a blue band, such as the Advanced Very High-Resolution Radiometer (AVHRR) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Alternatively, the EVI-2 (No.28–Table 3) with two spectral bands in the red and NIR regions was proposed, achieving similar performance to EVI, which can be produced by almost all optical sensors [71]. However, the EVI-2 is sensitive to the impact of the bidirectional reflectance distribution function (BRDF). Therefore, each index has advantages and disadvantages in relation to vegetation characteristics with overlapping spectral features due to background signals from soil and confounding factors (e.g., sensor and calibration effects, quality assurance and quality control, BRDF, and atmospheric and topographic effects). Hence, there are no perfect vegetation indices for all aspects of mangrove studies under all conditions.

3.2. Spectral Indices in Visible and Red-Edge Bands

The red edge is a band in the red-NIR transition zone that indicates the transition between red visible absorption by chlorophyll and NIR scattering due to leaf internal structure [25]. This transition zone is used in various vegetation indices, the most important of which is the normalised difference between red visible (0.6 nm) and NIR (0.8 nm) reflectance. Spectral indices obtained from the red edge region (

Table 4. The used indices with VNIR and red-edge bands (R: Red; B: Blue; and NIR: Near Infrared). With three Red-edge bands of Sentinel-2A, each formula can compute three sub-equations.

No.	Spectral Index	Formula	Reference
1	Normalised Difference Index [248]	$NDI=\frac{R_{edge}-R}{R_{edge}+R}$	[81,85,119,207,247]
2	Red edge NDVI [249]	$NDV{I}_{\mathrm{Re}d-edge}=\frac{NIR-R_{edge}}{NIR+R_{edge}}$
3	Plant Senescence Reflectance Index [250]	$PSRI=\frac{R-B}{{\mathrm{R}}_{edge}}$
4	Normalized Difference Red edge Index [251]	$NDRE=\frac{NIR-R_{edge}}{NIR+R_{edge}}$
5	Red-edge Chlorophyll Index [252]	$C{I}_{\mathrm{Re}d-edge}=\frac{NIR}{R_{edge}}-1$
6	MERIS Terrestrial Chlorophyll Index [253]	$MTCI=\frac{NIR-R_{edge}}{R_{edge}-R}$

) were used in several investigations for mangrove chlorophyll and biophysical parameters [81,119], mangrove density and carbon analyses [85,247], and mangrove biomass [207]. These investigations found strong correlation (r > 0.95) between ground truth data and red edge vegetation indices. Normally, red edge bands are available from hyperspectral datasets that are difficult for large-scale collection. Alternatively, since 2015, three red-edge bands have been incorporated in the Sentinel-2A sensor, which could be combined with other visible bands to provide essential information about mangrove ecosystems at a spatial resolution of 10 m. Therefore, the use of red-edge bands promises the potential to benefit mangrove ecosystems associated with mangrove health monitoring in the future.

3.3. Spectral Indices with Visible Bands of Airborne Systems

Traditionally, aerial photographs (AP) were widely used for mangrove mapping and assessment [94]. Nowadays, with the support of Unmanned Aerial Vehicles (UAVs) or drones, the application of aerial photographs has become more convenient. Some spectral indices for mangrove classification have been proposed using visible bands of aerial photographs that can achieve an overall accuracy of over 95% in mangrove cover mapping [157,254] (

Table 5. The used UAV spectral indices for mangrove ecosystems.

No.	Spectral Index	Formula	Reference
1	Excess Green Vegetation Index [248]	$ExG=2\times G-R-B+50$	[196,227,254]
2	Normalized Difference Index [248]	$NDI=\frac{G-R}{G+R}$
3	Negative Excess Red Vegetation Index [255]	$NegExR=G-1.4\times R$
4	Visible Atmospheric Resistant Index [256]	$VARI=\frac{G-R}{G+R-B}$
5	Colour Index of Vegetation Extraction [257]	$CIVE=0.441R-0.881G+0.385B+18.78745$
6	Vegetative Index [258]	$VEG=\frac{G}{R^{0.667}}\times B^{0.333}$
7	Excess Green minus Excess Red [259]	$ExG-ExR$
8	Triangular Greenness Index [260]	$TGI=G-0.39R-0.61B$
9	Combined Index [261]	$CI=0.25ExG+0.3ExGR+0.33CIVE+0.12VEG$
10	Visible-band Difference Vegetation Index [262]	$VDVI=\frac{2G-B-R}{2G+B+R}$

). Notably, integration of Red-edge, NIR, and shortwave infrared (SWIR) bands in the optical sensors of UAVs promises an advantage for monitoring mangrove ecosystems at a spatial resolution of centimetres. This drone-based multispectral remote sensing can be as the future for mangrove remote sensing. However, these applications are only suitable for a small area and can be employed in a short time due to cost and energy limitation. More importantly, the use of UAVs for field data collection is regulated by the government in most countries throughout the world.

3.4. Mangrove-Specific Spectral Indices

Mangroves are found in coastal wetlands where they are regularly submerged by tides [55,60,263]. As a result, fluctuations in tide levels, and thus the presence of water, results in tide-dependent variation in spectral signatures for mangrove forests, leading to inaccurate mapping results, particularly in locations with large tidal ranges [57,135]. Recently, several studies have developed specific spectral indices that can adapt to changes in tide conditions and applied them in order to separate mangroves from non-mangroves [57,58,60] and from other land cover types [55]. These are the mangrove-specific indices. During the 2013–2021 period, six mangrove-specific indices were proposed to improve the accuracy of single image remotely sensed data during high tide (

Table 6. The proposed mangrove indices for mangrove classification (EQN: equation number). In the Equation (2), GVI and WI are green vegetation index and wetness index at low (L) tide and high (H) tide, respectively. In Equation (4), n is the total number of bands in the image, R_i is the reflectance value at band i for a pixel of the reflectance image, and r_i is the reflectance value at band i for candidate spectrum of mangrove forest. In Equation (6), the ρ_λ is the reflectance of the band centre of λ, and i ranged from 1 to 4; λ₁, λ₂, λ₃, λ₄ represent the centre wavelengths at 705, 740, 783 and 865 nm, respectively. λ_i is the baseline reflectance in λ_i. ρ665 and ρ2190 are the reflectance of band 4 (centred at 665 nm) and 12 (centred at 2190 nm), respectively. In Equations (3), (5), and (7), G, R, NIR, and SWIR are green, red, near-infrared, and shortwave infrared bands, respectively.

Index	Formula	EQN	Reference
Mangrove Recognition Index	$MRI=\left|GV{I}_L-GV{I}_H\right|\times GV{I}_L\times (W{I}_L+W{I}_H)$	(2)	[57]
Mangrove Damage Index	$MDI=\frac{NIR-SWIR}{NIR\times SWIR}\times 10,000$	(3)	[56]
Mangrove Probability Vegetation Index	$MPVI=\frac{n{\displaystyle \sum _{i=1}^n{R}_i{r}_i-{\displaystyle \sum _{i=1}^n{R}_i{\displaystyle \sum _{i=1}^n{r}_i}}}}{\sqrt{n{{\displaystyle \sum _{i=1}^n{R}_i^2-\left({\displaystyle \sum _{i=1}^n{R}_i}\right)}}^2}\sqrt{n{{\displaystyle \sum _{i=1}^n{r}_i^2-\left({\displaystyle \sum _{i=1}^n{r}_i}\right)}}^2}}$	(4)	[59]
Combined Mangrove Recognition Index	$CMRI=\frac{NIR-R}{NIR+R}-\frac{G-NIR}{G+NIR}$	(5)	[58]
Mangrove Forest Index	$\begin{array}{l}MFI=[\left({\rho }_{\lambda 1}-{\rho }_{B\lambda 1}\right)+\left({\rho }_{\lambda 2}-{\rho }_{B\lambda 2}\right)+\left({\rho }_{\lambda 3}-{\rho }_{B\lambda 3}\right)+\left({\rho }_{\lambda 4}-{\rho }_{B\lambda 4}\right)]/4\\ {\rho }_{B\lambda i}={\rho }_{2190}+\left({\rho }_{665}-{\rho }_{2190}\right)\times \left(2190-{\lambda }_i\right)/\left(2190-665\right)\end{array}$	(6)	[60]
Mangrove Vegetation Index	$MVI=\frac{NIR-G}{SWI{R}_1-G}$	(7)	[55]

).

Zhang and Tian (2013) [57] proposed a mangrove recognition index (MRI–Equation (2)) for mangrove detection using multi-temporal Landsat TM images that is insensitive to the stage of the tide. Winarso et al. (2014) [56] further developed this method to create a mangrove discrimination index (MDI–Equation (3)) for estimate mangrove density from Landsat 8 images. Kumar et al. (2017) [59] proposed two new vegetation indices (Normalised Difference Wetland Vegetation Index and Shortwave Infrared Absorption Index) and combined them with two previously published indices (Normalised Difference Infrared Index and Atmospherically Corrected Vegetation Index) to integrate the Mangrove Probability Vegetation Index (MPVI–Equation (4)) for mangrove classification. Gupta et al. (2018) [58] published a Combined Mangrove Recognition Index (CMRI–Equation (5)) to distinguish mangrove from non-mangrove with an accuracy of more than 60%. Jia et al. (2019) [60] generated the Mangrove Forest Index (MFI–Equation (6)) using spectral bands from Sentinel-2 data to detect submerged mangrove forests at high tide.

These proposed mangrove indices involve the signature of mangroves in the context of tidal fluctuation, which is sensitive to greenness and wetness patterns. Thus, MRI and MPVI are sensitive to tidal extent and period, and cannot be used in site comparisons where sites differ in hydrology. Additionally, the number of spectral bands required for the MFI calculation is only available using Sentinel-2 or hyperspectral sensors. Baloloy et al. (2020) [55] recently analysed the shortcomings of earlier integrated mangrove forest indices (i.e., MRI, CMRI, MPVI, NDI, and MFI) and developed a new index: the mangrove vegetation index (MVI–Equation (7)), for enhancing the accuracy of mangrove forest extent mapping. MVI is a single index that classifies mangroves, terrestrial vegetation (forest and non-forest), bare soil, built-up areas, water, and clouds using reflectance data from Sentinel-2A and Landsat-8 in the NIR, Green, and SWIR bands. MVI validation was initially used at an intercontinental scale and demonstrated an accuracy of more than 80% for the entire set of geographical research locations. The high index accuracy of MVI can provide a possibility for global mangrove studies, although MVI is limited by biophysical and environmental parameters due to its reliance on SWIR. Using the SWIR spectrum, in particular, has been a problem for sensor systems constructed with solely visible and NIR wavelengths (e.g., Landsat-1,4 and Planetscope). Additionally, SWIR reflectance value is frequently mixed with built-up land noise, water bodies, and vegetation surrounding [61,264,265]. Neri et al. (2021) [266] investigated misclassification of mangroves from other land cover types in aquaculture zones, irrigated croplands, and palm tree sites when applying MVI due to spectral similarity between mangroves and vegetation in these areas. Notably, the significant drawback of MVI is that it does not have a specific optimal threshold for mangroves, which differs from ranges of vegetation and other mangrove-specific indices for mangrove separation.

In summary, there are several newly improved spectral indices for mangrove classification, but none of these can completely reduce the impact of environmental factors (e.g., tidal influences, land cover mixture).

4. Evaluation of Spectral Indices Applications in Mangrove Remote Sensing

As mentioned in Section 3, a variety of spectral indices have been used for (i) mapping mangrove extents and distributions; (ii) measuring above-ground properties of mangroves; and (iii) detecting mangrove changes. The co-word map of keywords that resulted from the co-word analysis of the literature on spectral indices application for mangroves is presented in Figure 4. The mangrove and forest were core words of the network, and NDVI and remote sensing were prominent terms in a field study. This showed that NDVI has a strong relationship with aspects of mangrove remote sensing in terms of spectral index application. Also, the result predicted that NDVI was the most common index applied to mangrove ecosystems. In total, 82% of our reviewed publications used NDVI and 28% used EVI.

4.1. Mangrove Extent and Distribution

Mapping mangroves is important for recording the present mangrove area and species distribution, provides information to support management decisions around potential threats of degradation due to uncontrolled development, and enables modelling change in relation to driving factors. Mangrove mapping can be binary, which classifies an image into two classes: mangroves and non-mangroves, or into a number of land covers based on a particular land over classification scheme with mangrove as one of the land cover types. Binary mapping normally uses single spectral indices (e.g., NDVI, EVI, SAVI, or mangrove indices) to highlight the pixels of mangrove vegetation and separate them from non-mangrove pixels [58]. Mapping can also include continuous classifiers such as height and canopy area.

Image classification for mangrove mapping can be supervised or unsupervised, pixel-based or object-based. Almost all previous research applied composited bands of satellite images based on classification approaches such as unsupervised algorithms (e.g., ISODATA or K-means) and parametric supervised algorithms (e.g., maximum likelihood) to detect mangrove extents without spectral indices application [25,26,267]. The overall accuracy of post-classification from these approaches ranges from 42% to 68% for most optical sensors and methodologies to measure accuracy [26]. Since spectral indices were used for mangrove ecosystems under pixel-based or object-based categories based on machine learning algorithms (e.g., artificial neural network, support vector machine, and random forest), an improvement of post-classification overall accuracy has been achieved to more than 80% [26]. More recent studies have used mangrove classification using spectral indices based on random forest, one of the machine learning algorithms, and offer the highest post-classification overall accuracy (>92%) [27,196,268].

From using single spectral indices for mangrove separation, some studies found that in dense mangrove areas, the NDVI or EVI value threshold for discriminating mangroves was 0.3 and above [104,269]. Le et al. (2020) applied NDVI, derived from Sentinel-2, to detect mangrove cover in the Can Gio Mangrove Biosphere Reserve, Vietnam [183]. This study considered that the NDVI mangrove value as NDVI > 0.3 with an overall classification accuracy of 83%. In addition to NDVI/EVI, six mangrove spectral indices (Table 6) have been developed and applied for mangrove separation with an overall classification accuracy of 80% above [55].

Vegetation, soil, and water are the three principal factors that contribute to the pixel composition of remotely sensed data in mangroves (Figure 5). In addition, seasonal and diurnal intertidal interactions influence the surface appearance [13,263]. These factors have a significant impact on the spectral characterisation of picture components. Therefore, depending on the effects of natural surroundings and mangrove density, the NDVI/EVI thresholds can be adjusted. In addition to physical influences, mangroves and other types of vegetation may generate similar signals from the vegetation index [56]. As a result, utilising a single NDVI or EVI threshold to distinguish mangroves from other types of vegetation may lead to an inaccurate outcome. Therefore, several soil and water spectral indices (

Table 7. The indices in the Visible, Near Infrared (NIR), and Shortwave Infrared (SWIR) bands.

No.	Spectral Index	Formula	Reference
1	Normalized Difference Moisture Index or Land Surface Water Index [264,270,271]	$NDMI=LSWI=\frac{NIR-SWIR}{NIR+SWIR}$	[59,60,91,123,134,135,152,191,198,201]
2	Normalised Difference Water Index [264]	$NDWI=\frac{R-SWIR}{R+SWIR}$	[97,105,138,150,195,206]
3	Modified Normalized Difference Water Index [272]	$MNDWI=\frac{G-SWIR}{G+SWIR}$	[60,134,201]
4	Normalised Difference Soil Index [105]	$NDSI=\frac{SWIR-NIR}{SWIR+NIR}$	[105]
	Normalized Difference Water Index [61]	$NDWI=\frac{G-NIR}{G+NIR}$	[58,130,195,196,198,201]

) have been used concurrent with vegetation indices to improve mangrove detection [150,198].

The reflected signals from the SWIR regions capture information on radiation absorption by water, cellulose and lignin, and a variety of other biological elements. Nevertheless, collecting satellite imagery at SWIR wavelengths has distinct advantages, such as better atmospheric penetration and better contrasts among different vegetation types. The utilisation of the SWIR band in conjunction with the visible and NIR bands, in particular, aids in enhancing the presence of water in plant leaves [264] or urban characteristics [265]. For example, NDWI and NDBI threshold values more than 0 visualise water bodies and impervious surface, respectively [61,264,265]. Besides, several studies used elevation data and tasselled cap transformation to further improve the classification accuracy [55,57,134]. Consequently, an improved performance (≥90% of overall accuracy) was investigated when apply multiple spectral indices for detecting the mangrove cover [27,267].

Our search did not reveal a spectral index that has been applied for mangrove species separation. Previous studies applied spectral bands to separate mangrove species based on maximum likelihood classification or machine learning algorithms (e.g., random forest and support vector machine) because each mangrove specie reflects a particular wavelength of the spectrum [25,26,27,164,273]. These studies revealed that spectral reflectance properties of some mangrove species are similar, making a challenge for identification. Hirata et al. (2014) [273] proved that the spectral reflectance properties for A. alba and S. alba were clearly distinct in three of four VNIR bands (i.e., Green, Red, and NIR), whereas those for the Rhizophora and Bruguiera species were similar in most spectral bands. A similarity of spectral bands among mangrove species leads to a uniformity/resemblance as using spectral indices because spectral index is computed from spectral bands ratio. It shows that almost all of mangrove species also have the same threshold value in spectral index. For instance, red mangroves (Rhizophora), black mangroves (Avicennia), and white mangroves (Laguncularia racemosa) may have particular spectral reflectance in a single spectral band, but the signals of three species in NDVI/EVI are normally more than 0.3.

4.2. Above-Ground Properties of Mangroves Estimation

The term “above-ground mangroves properties” in our study refers to the estimation in aspects of mangrove ecosystems above ground such as leaf area index (LAI), biomass, carbon, vertical structure, and mangrove health. Understanding these variables is beneficial in detecting the interaction of vegetation, the stability of that interaction, and the change in mangrove population [7,263,274]. Historically, the majority of research on these parameters’ estimation employed ground-based approaches that were time-consuming, costly, and distribute sparsely across space, making regional mangrove monitoring challenging [25]. Alternatively, a number of papers predicted mangrove above-ground properties using vegetation indicators [177,209,220]. These research applied regression analyses to establish empirical relationships between remotely sensed vegetation indices and measured-above ground mangrove (AGM) data (e.g., leaf area index, height canopy, carbon sink, and biomass) [207,209,275,276]. These studies considered that indices derived from satellite data successfully modelled and estimated the mangrove above ground features. However, above ground mangrove ecosystems today have not been compared with the patterns of 30–50 years ago.

In these studies, NDVI and EVI demonstrate the most explanatory curvilinear relationships with AGM [25,65,93]. In fact, there is a saturation issue with NDVI that is mainly due to the red band, the energy in which is strongly absorbed by pigments. When a leaf contains a certain number of pigments, the reflectance remains low and practically constant with more pigment (e.g., increased leaf area) [70,214]. As a result, where forests are high in biomass, NDVI struggles to differentiate moderately high plant cover from very high plant cover [153,214]. Since 2012, most studies confirmed that EVI indicated a higher correlation coefficient with mangrove in field measurements than NDVI to significant extents [59,153,169]. Meanwhile, a few publications found NDVI to be the best mangrove predictor, relative to the performance of EVI and other vegetation indices [80,144,160]. The scale of the analysis also affects the suitability of the index used, with a few studies finding that at a higher resolution (smaller scale) NDVI is the preferred index [84]. In fact, each ecosystem has its unique characteristics, and each index is a separate indication for green vegetation. The best vegetation index to use for AGM estimation varies and thus its selection requires substantial field measurements to validate the results.

4.3. Mangrove Changes

Understanding of variations in mangrove patterns is critical in providing fundamental source for proposing appropriate strategies in mangrove ecosystem management and serving as a reference for broader worldwide applications. The changes in mangrove ecosystems are acknowledged as a result of natural influences and human activities. To investigate mangrove changes, two methodologies are usually applied (i) bi-temporal analysis and (ii) long-term monitoring. Bi-temporal analysis uses two images per 5 or 10 years to assess the changes in mangrove cover. A bi-temporal technique is common and easy to apply, and it calculates the differences of mangrove cover at two times in the context of land use and land cover change. However, there are some limitations in regard to environmental factors (e.g., tide variations, terrain, and atmospheric conditions) if images are only obtained on a single day of the year. This is because we can utilise the method outlined in Section 4.1 to retrieve information about land cover for each year before employing an intersection of two scenes. Besides, the significance of physical factors (e.g., erosion, typhon) as a source of mangrove loss and the trend in mangrove cover may be underestimated.

In contrast, long-term monitoring normally applies time series of spectral indices to understand mangrove dynamics through space and time. Most of studies applied NDVI or EVI and linear regression algorithms to analyse spatiotemporal change and anticipate trends in mangrove distribution at a local scale [172,183,214,277]. These analyses concluded that the loss of mangrove ecosystems is mainly caused by conversion in land use and land cover, compared to natural factors. The aquaculture ponds and impervious surface expansion in the coastal area are a threat to mangrove ecosystems [28]. Globally, Hansen et al. (2013) [277] first used NDVI and ordinary least squares slope of the regression to examine forest loss and gain from 2000 to 2012. The study revealed that a decreased trend in mangrove cover occurred in Asian and Caribbean countries. However, the global analysis overlooked driving factors (e.g., land use and land cover transformation and physical hazards) because policies for land use and land cover changes are different among nations in the world.

Overall, the application of spectral indices for mangrove remote sensing provides several advantages in relation to mapping spatial distribution, above-ground mangrove properties, and mangrove changes. However, examples of knowledge gaps from previous studies should be included (i) visualising mangrove changes from the past to the present; (ii) identifying the driving elements impacting mangroves; and (iii) evaluating effects of environmental factors on satellite images.

5. Discussion and Future Directions

5.1. The Potential Indices for Mangrove Remote Sensing

Over the past 50 years, the importance of spectral indices in mangrove remote sensing has been recognised, but several knowledge gaps still exist in relation to the best index selection for mangrove characterisation. A perfect spectral index for an ensemble of mangrove biophysical parameters is yet to be developed. Our study explored that NDVI accounted for the highest proportion (82%) of the applied spectral indices for mangrove ecosystems, followed by EVI (28%). These normalised algorithms mitigate the atmospheric effects and the impacts of sensor calibration degradation in the red and NIR bands [64,66]. The widespread adoption of remote sensing has resulted in the creation of low-cost image data that may be used to broaden NDVI applications. Hence, NDVI will continue to be a dominant vegetation index used for mangrove remote sensing. However, this does not mean that NDVI is always effective because of its limitations in relation to soil background and vegetation density.

Each index has its own advantages and disadvantages and can be affected by the impacts of the soil background and confounding factors such as sensor and calibration effects, bidirectional reflectance distribution function, atmospheric and topographic effects, or other local environmental conditions (e.g., tide). Hence, for future applications, instead of constructing or discovering a prospective mangrove index, we should examine local conditions and the factors influencing the effectiveness of spectral indices before deciding on the use of them for analysis. Additionally, the number of spectral bands available on optical sensors influenced the indices chosen for mangrove remote sensing. For instance, the four-band version of the Planetscope instrument with no SWIR band is only able to produce spectral indices in the visible and NIR regions. In the case that one index cannot meet the needs of mangrove assessment or other purposes, another index should be applied.

5.2. Long-Term Mangrove Monitoring with Time Series-Based Approaches in Relation to Driving Factors

Monitoring mangrove dynamics normally includes seasonal and annual changes that require a series of historical and regular imagery. In fact, there are many factors, including tide conditions, atmospheric factors, or missing or mis-registered data, that can cause errors in image acquisitions. Therefore, using single-date images to calculate spectral index has shown significant limitations on a large scale because environmental conditions vary from day to day and across sites. Alternatively, generating optical images using averaging is less susceptible to high resolution noise and are thus capable of characterising both long-term and abrupt mangrove changes. For example, using annual mean/median spectral indices that are derived from daily/5-days/8-days/16-days timeseries data enables us to reduce the environmental factors’ influence on the image of interest. The study of multiple remotely sensed data has been widely employed in phenological investigations of mangrove ecosystems [121,134,153].

In addition to data, time series analysis provides pieces of information on the timing of mangrove change, as well as improving the quality and accuracy of information being derived using remotely sensed data [167,172,174,217,219]. Also, the time series analysis of spectral indices data evaluates trends and predicts the persistence of mangrove trends under spatial regression application. A variety of time series analysis techniques have been produced [(e.g., National Forest Trend [278], Recurrent Neural Network [279] to analyse and monitor spatiotemporal changes in mangrove ecosystems [191,221,280]. The digital number (DN) value of each pixel from time series images gives more sensitivity than single composited spectral band so that it can easily compare with natural factors (e.g., rainfall, temperature, and ocean dynamics) to certain significance of physical influences. This method holds significant promise for studying the long-term dynamics of environmental variations, and it can monitor future mangrove regeneration.

5.3. Fusion of Images from Multiple Sensors

In the process of the Earth’s surface observation and particularly in mangrove remote sensing, selecting a potential optical sensor to calculate spectral index is crucial for assessment with high accuracy. However, there are several factors that can influence the choice of optical remote sensing platforms, such as the purpose of the research, the data availability, the national context, the budget constraints, the scale of the study, and the location of the study area. For instance, to explore spatiotemporal changes in annual mangrove patterns, a long-term time series analysis from Landsat imagery should be preferred because the data is available from 1972 to date. Additionally, to understand mangrove quality or seasonal changes, a variety of MODerate Resolution Imaging Spectroradiometer (MODIS) products with a high temporal resolution (1 day) may be the best choice. Recently, several studies fused multi-sensor images to have more information about mangrove ecosystems. For example, Kanniah et al. (2021) [281] used three optical sensors (i.e., Landsat, MODIS, and Sentinel-2A) to study mangrove fragmentation and health conditions. Guo et al. (2021) [209] used UAV and WorldView-2 datasets to validate the Sentinel-2 imagery for LAI estimation. Besides, several publications fused passive and active sensor images to understand mangrove structure or biomass. Pham et al. (2020) [89] combined optical bands (Sentinel-2A) with active sensors (i.e., Sentinel-1 and ALOS-2 PALSAR-2) to calculate some vegetation indices for mangrove above-ground biomass. These studies concluded that fusing multiple remote sensing sources helps to provide a large amount of information about mangrove ecosystems, compared to single sensor applications.

Fusion of multiple sensors can enhance the accuracy of the data. Integration of NDVI from Advanced Very High-Resolution Radiometer (AVHRR–launched in 1979) and MODIS (launched in the 2000s) enabled a long-term dataset from 1979 to date. AVHRR NDVI composites at 1 km spatial resolution [92,93] was used for mangrove monitoring prior to the 2000s. However, the AVHRR satellite system has degraded in orbit to the point that it is advised that NDVI MODIS products should be used for longer periods in the future [282]. Additionally, combination of ASTER (launched 1999) and Landsat 4,5, 8, and 9 is an alternative approach for line correction of Landsat 7, allowing us to obtain a set of data at 30 m spatial resolution from 1988 to the present. Notably, using multiple sensors enables improving the re-visit days of satellite data, which is better for smoothing data [25,27]. For example, when Landsat 8 and Landsat 9 are combined, the re-visit days are reduced from 16 to 8 days. Hence, fusion of multiple sensors (i.e., passive, and active sensors) is a recommendable approach to compute the spectral index for mangrove studies in the future.

6. Conclusions

Land use and land cover transformation in relation to natural hazards are the primary factors threatening mangroves in the future. Spectral indices have been applied to mangroves and demonstrated their effectiveness in various studies over 50 years. Each spectral index has its own strength and limitation in mapping mangrove distributions and measuring their above-ground biophysical properties in various environments. Therefore, to select a potential index, we should understand the interaction between the local conditions and mangrove ecosystems. NDVI is the most popular index that can be applied for mangrove ecosystems, followed by EVI, although both are sensitive to environmental conditions.

Long-term mangrove monitoring is crucial for identifying the trend in mangrove pattern changes in connection to driving variables. Using time series analysis of spectral indexes helps to reduce the effect of external influences. Using multiple sensors enables obtaining a set of databases for long-term monitoring associated with natural hazards and human activities. Nowadays, accessing big data has become easier with the help of technology and digital cloud platforms (e.g., Google Earth Engine). These technological advancements will shift mangrove studies from a local to a global scale and imply the necessity to learn programming skills.

In the context of the digital era, mangrove scholars should apply the advantages of cloud computing platforms for spectral index computation. These approaches assist image processing quickly and enable analysis of mangrove ecosystems at global scale. However, approaching these techniques requests users to have some knowledge about information technology and quite understand about coding. Therefore, developing a tool or application on cloud storage for mangrove monitoring based on vegetation index should be taken into account for new users or scholars who do not have good information technology skills. In addition to tools, a guideline for algorithm selection (e.g., machine learning, deep learning) should be developed to save time for spectral index computation.