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Review

Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis

by
Fejaycris Pillodar
1,
Peter Suson
1,
Maricar Aguilos
2,* and
Ruben Amparado, Jr.
1
1
Environmental Science Graduate Program, Department of Biological Sciences, College of Science and Mathematics, Mindanao State University-Iligan Institute of Technology, Iligan City 9200, Philippines
2
Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA
*
Author to whom correspondence should be addressed.
Forests 2023, 14(6), 1080; https://doi.org/10.3390/f14061080
Submission received: 11 February 2023 / Revised: 13 May 2023 / Accepted: 21 May 2023 / Published: 24 May 2023
(This article belongs to the Special Issue Forest Vegetation Monitoring through Remote Sensing Technologies)
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Abstract

:
In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study intends to study the trend in mangrove resource mapping using remote sensing (RS) to determine the appropriate methods and datasets to be used in monitoring the distribution, aboveground biomass (AGB), and carbon stock (CS) in mangroves. A meta-analysis of several research publications related to mangrove resource mapping using RS in the Philippines has been conducted. A database was constructed containing 59 peer-reviewed articles selected using the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework and preferred reporting items for systematic reviews and meta-analysis (PRISMA). The study clarified that support vector machine (SVM) has shown to be more effective (99%) in discriminating mangroves from other land cover. Light detection and ranging (LiDAR) data also has proven to give a promising result in overall accuracy in mangrove-extent mapping (99%), AGB, and CS estimates (99%), and even species-level mapping (77%). Medium to low-resolution datasets can still achieve high overall accuracy by using appropriate algorithms or predictive models such as the mangrove vegetation index (MVI). The study has also found out that there are still few reports on the usage of high-spatial-resolution datasets, most probably due to their commercial restrictions.

1. Introduction

Mangrove forests are coastal wetlands and considered to be among the most productive biomes on earth, providing diverse ecosystem services, such as provisioning (e.g., food source, livelihood options), regulating (e.g., recreation, spiritual), and supporting (e.g., habitat, nutrient cycling) services [1,2,3,4,5,6]. Specifically, these essential ecosystems offer vital protection for coastal and marine ecosystems, including coastal communities, against abrasion [7], reducing tsunami impacts [8], providing an ecosystem for flora and fauna [9], and acting as a sink for carbon dioxide [10,11]. From a global perspective, mangrove ecosystems, which to a large extent are found in tropical countries, make up 18.1 million ha of the wetlands’ landscape [12]. Despite the significance of mangroves, they are still threatened by a variety of factors, including deforestation, land-use change, and coastal development [13,14,15]. In the Philippines, during the 1920s, mangrove cover was estimated to be between 400,000 to 500,000 ha but had decreased by more than 50% by the year 2000 [16,17]. Mangroves are subject to change through time due to their dynamic nature and are particularly vulnerable to deforestation for aquaculture conversion and road development [18,19,20,21]. Due to the dramatic decline in the extent of mangrove forests, monitoring their condition has become an important concern in recent years [22,23]. To monitor mangrove forests, field inventory is the method that has been commonly used for many years [24]. However, sampling mangrove forests is very challenging because they are in inaccessible areas because of their environment, and the structures of their pneumatophores and prop roots provide additional barriers. Remote sensing (RS) technology makes it possible to monitor and map enormous areas for extended periods at a much lower cost, faster rate, and on a larger scale than traditional field measurements [25,26]. While several studies related to mangroves and RS have been conducted at a global scale, some other researchers and government agencies in the Philippines are still uncertain about using such technology.
A meta-analysis is defined as a subset of systematic reviews used for quantitative research syntheses of multiple studies. It is also a research synthesis that uses a quantitative measure, effect size, to indicate the strength of the relationship between the treatments and dependent measures of the studies making up that synthesis [27]. Additionally, meta-analysis is a set of statistical methods for combining the magnitude of outcomes (effect sizes) across different datasets addressing the same questions. It also helps to identify gaps in the literature where more research is needed, and to identify areas where the answer is definitive, and no new studies of the same type are necessary [28]. Meta-analysis has been used in several environmental studies such as determining the trend of wetland classification using RS, in which authors have observed that one of the main challenges in using high-resolution imagery is its availability, whereby some protocols may be impractical to implement at the regional, let alone national or continental scale [29]. A meta-analysis has also been used in the comparison of overall accuracy in mangrove extent and showed results that optical RS and radar data fusion could effectively improve the overall accuracy (>90%) of extent identification [28]. The same study concluded that overall accuracy in mangrove species identification ranged from 68% to 98%. It was observed that the closer the spatial resolution is to the size of the plant canopy, the higher the accuracy of species identification. Additionally, a comparison of different RS platforms and sensor types was analyzed for aboveground biomass accuracy estimates, and it was found that LiDAR datasets used in a study produced the lowest errors when used in a synergistic manner with other coincident multi-sensor measurements [30]. Through meta-analysis, several key future directions have been identified for the potential use of RS techniques combined with machine learning techniques for mapping mangrove areas and species and evaluating their biomass and carbon stocks [31]. A few studies [32,33] have discussed mangrove RS using meta-analysis in recent years from a global perspective. However, few of these were exploring the status of different RS approaches in mangrove resources to address the regional level of understanding of the knowledge gaps in this field of research. Using 59 published articles, this paper aims to examine the trend of usage of RS in mangrove resource monitoring studies and inspect the results on its advantages and disadvantages for mangrove resource mapping in the Philippines. Peer-reviewed articles were collected and analyzed and compared in commonality, such as data type, sensor platform, and methods, in terms of overall accuracy. This paper answers questions about the status of the usage of RS technology in mangrove resource mapping in the Philippines and assumes that there are new methods that would revolutionize the discrimination of mangroves from different land cover and examines future opportunities for mangrove conservation and management strategies.

2. Materials and Methods

To achieve the main objective of this study, we conducted a thorough search in Google Scholar, Web of Science, and Mendeley in which we can easily find other studies related to the subject matter and be directed to other relevant article databases such as Researchgate, Elsevier, and Springer. There were 59 key studies identified in this work from the selection and documentation of published peer-reviewed articles. The method generally used in this study was the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework [34]. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) [35] were also utilized. Most of the peer-reviewed articles were published in the same journals and conferences. The search was limited to articles published between 1979 and 2022 that used English keywords related to mangrove monitoring using RS in the Philippines. Figure 1 depicts a word cloud of keywords used in RS mangrove monitoring studies; the text size indicates the frequency of term usage.
A search query was designed that would encompass all the articles with fundamental keywords in mangrove studies using RS in the Philippines. To obtain a comprehensive collection of related articles, the query with the keywords “mangrove* AND remote* sens* AND Philippines” (MSRP) was used to filter and exclude articles that did not share a common attribute with other articles for meta-analysis. In the initial stage, a total of 2127 records were found from Google Scholar (1980), Web of Science (106), and Mendeley (41), with 28% overlapped articles. After removing works of literature such as gray literature, extended abstracts, presentations, keynotes, book chapters, non-English language papers, and inaccessible publications, the number of studies was reduced to 236 retained for further title reading. After a thorough evaluation of the articles based on data types, platforms used, methods, and overall accuracy, only 122 articles fulfilled the criteria for further abstract and main body reading. Among them, 37 articles were taken for MSRP analysis since the scope of this study was limited only to the Philippines. This low number of published articles was expected, however, an additional step was taken to verify if there are still relevant articles to be included in the analysis.
This step involved the collection of all of the main and co-authors selected for the MSRP analysis and then searched for their other related studies. If their studies were relevant to the topic and eligible for further analysis, then they were included, which caused a “snowball” effect. In the end, 59 publications which fulfilled the inclusion criteria for meta-analysis were retained. The process of selecting relevant applications based on the PSALSAR flow diagram is depicted in Figure 2.
Table 1 shows the attributes extracted from the research papers. These attributes were then tabulated and analyzed to provide a general picture of how RS has been used across mangrove ecosystem studies in the Philippines. One of the important attributes explored across the selected articles and being used for this meta-analysis is the sensor type, which directly impacts the resolution, accuracy, area coverage, data types, and data output. Data types encompass the band wavelengths acquired from different sensors (e.g., Landsat 5 TM, Landsat 7 ETM+, Sentinel-2 MSI) used as input to different algorithms to determine the mangrove extent, and to calculate the height, diameter at breast height (DBH), aboveground biomass (AGB), and carbon stock (CS). To compare differences in the overall accuracies of each sensor used in mangrove ecosystems mapping, the Wilcoxon test, using the PAST software v4.03, was utilized [36].

3. Results

3.1. Trend of Mangrove Resource Mapping Using RS in the Philippines

Figure 3 illustrates how mangrove ecosystem studies using RS have increased over recent decades, with an annual trend that is approximately exponential. However, years without any published papers related to mangroves and RS are not included in this figure. Based on our database, the first article on mangroves and RS was published back in 1979. Starting from 2015, there has been a sudden rise in mangrove and RS-related topics. Since then, the number of published articles related to mangroves and RS had a positive rise and fall trend compared to the previous years. The upward trend in published articles on mangroves and RS is due to the increased availability of RS sensors in recent years. An increase in mangrove forest loss and the initiative of academic institutions are also some of those important factors contributing to the increase in the number of publications [18,37,38,39,40].
As shown in Table 2, the first article was published in the Photogrammetric Engineering and Remote Sensing journal by Lachowski et al. [41]. Their study focused on forest land cover assessment of the Philippine Islands, which also includes mangrove ecosystems. They utilized Landsat 1 and 2 in this study because Landsat 1 had limited recording capabilities during 1974–1976 and dense cloud cover often coincided with Landsat overpasses. During the late 1900s and early 2000s, a minimal number of articles were published for different objectives and methods such as spectral vegetation indices (SVI) and leaf area indices (LAI) [42], forest mapping with the use of Landsat and unsupervised classification [16], calculating rates and patterns of the forests (e.g., mangrove forest) in the Philippines using geographic information systems (GIS) [43], mapping mangrove forest land cover using Landsat and decision tree (DT) classification [44], and the development of brackish aquaculture and mangrove reforestation planning [45], and the monitoring of mangrove areas affected by an oil spill in TINMAR, Guimaras [46] was implemented with the aid of GIS during the early years of the Philippine GIS and RS revolution.
The number of programs and initiatives conducted by institutions together with the funding agencies are shown in Figure 4. It indicates that the DOST-PCIEERD has the most funded research papers from 2015 to 2021. This is due to the national-based program launched by the national government and in cooperation with other foreign agencies. Based on the products and results produced by LiDAR technologies in the Phil-LiDAR 1 project in flood hazard mapping [47,48], government agencies and the commercial sector in the Philippines have acknowledged the accuracy of LiDAR technology. Thus, the need for precise and accurate extraction of natural resources such as forests, which include mangroves, and high-value crops as baseline for mapping is vital.
The geographical locations shown in Figure 5 are based on the articles reported in each study. Each instance in which a province was included in the conduct of the study is counted as one. The figure shows the total instances where a certain scientific study was conducted throughout the country. Most of the studies in this paper are concentrated in one province. However, there are several studies that chose to cover multiple areas [49,50,51,52,53,54,55]. On top of that, there are several authors who chose to cover areas at the national level [16,39,41,43,44,56,57].
According to Giri et al. [58], the Philippines contains 1.9% of the world’s total mangrove area. However, the extent of mangrove disturbance is uncertain. Therefore, the need for updated studies on mangrove resources is vital. The province of Palawan has the greatest number of articles relating to any single province (18 studies); it has the largest mangrove forest cover in the country, with a total area of 54,457 ha [44]. Other provinces with numerous studies include Batangas and Iloilo (15 studies), Aklan and Zambales (14 studies), Eastern Samar and Lanao del Norte (13 studies), and 12 studies were conducted in five provinces including Bataan, Cavite, Oriental Mindoro, Quezon, and Guimaras. These studies found a significant and decreasing pattern of mangrove loss [56].

3.2. Early Years of Mangrove Resource Extraction Using RS

A summary of the use of RS in mangrove resource mapping in the Philippines is shown in Figure 6 and grouped according to topics, mapping purposes, and resolution of sensors. The timeline was divided into a 10-year interval, starting from the years 2022 and 2012, 2012 and 2002, and 2002 and earlier. The history of mapping mangrove extent with RS can be traced back to the 1970s. The work of Lachowski et al. [41], published in the Photogrammetric Engineering and RS journal, can be considered one of the pioneering studies conducted on remotely sensed data on forest cover in the Philippines. During this year, the Department of Natural Resources (DNR) of the Philippines (now DENR) undertook the task of identifying trends in land cover changes caused by the increased utilization of the nation’s forests. Between 1979 and 2002, all mapping objectives were limited to extent mapping, change detection, and rehabilitation of mangrove forests. There were three studies conducted between 1979 and 2002, but only one study with an accuracy assessment included was conducted within these years, using Landsat 1 and 2 MSS, SPOT, and Landsat 5 TM sensors [41,42,45]. Subsequently, for the next ten years (2002–2012), the three studies conducted were focused on mangrove-extent mapping (the first national-scale mangrove forest mapping) and the impact of an oil spill on mangroves and other coastal resources with the use of the Landsat 8 OLI, ASTER, and Worldview-1 sensors [17,42,46]. These sensors range from low (≥30 m) to high (≤10 m) resolution. In the utilization of these sensors, the combination of RGB, SWIR, NIR, and panchromatic bands helped to obtain better accuracy results. After this, Long et al. [44] provided the first truly national mangrove monitoring assessment and change detection in the Philippines, from 1990 to 2010. The study used Landsat images and the DT algorithm to classify mangroves and found a 10.5% decrease in mangrove forest cover from 1990 to 2010. This study also provided the most up-to-date and reliable data on the Philippines’ mangrove area and spatial distribution, as well as delineating where and when mangrove change occurred in previous decades, prior to 2014.

3.3. Expansion of RS Uses in Mangrove Resources Mapping

In the year 2015, researchers from the country started using high-resolution datasets from airborne LiDAR sensors. Other sensors such as Sentinel-1, Sentinel-2, RapidEye, PlanetScope, Landsat 7, Landsat 8, SAR, SRTM, and ALOS/PLASAR, ranging from low to high-resolution data, were also used coupled with different classification algorithms. Research objectives from 2015 onwards have also expanded from the common mangrove-extent mapping to species-specific mapping [59]. LiDAR can classify mangroves into species-specific levels. However, results have shown the need for an accuracy improvement (77%), which can be achieved with the use of appropriate algorithms. Studies such as aboveground biomass and carbon stock estimation [40,48,49,60,61,62,63,64,65], 3D visualization [66,67], stand forecasting [68,69], and LAI [70] are evolving and continue up to the present time. These demonstrate that studies on mangroves with the use of RS have become more common and popular in recent years.
All research articles retrieved in this study were grouped into two categories: (1) before 2015, most studies were focused on mangroves’ forest extent mapping by exploring different types of RS data and algorithms [16,42,44]; and (2) from 2015 onwards, studies that have the intention to map mangrove ecosystem characteristics and biophysical aspects have drawn more attention [40,49,65].

3.4. RS Data Types

Before 2015, image classification approaches struggled to discriminate mangrove species due to spatial and spectral constraints. From 2015 onwards, published articles related to mangroves and RS were devoted to mangrove-extent mapping [55,71,72] and biophysical and ecosystem characterization [61,70,73]. The accuracy and precision of available RS data, regardless of the resolution, have prompted many researchers to bring remotely sensed mapping to the next level. Argamosa et al. [49] were able to produce acceptable AGB estimates using the information derived from Sentinel-1 C-band SAR data in San Juan, Batangas (dense mangrove forest) and Masinloc, Zambales (sparse mangrove forest). The features used to model AGB were combinations of polarisations (VV, VH), its derivatives, gray level occurrence matrix (GCLM), and its principal components. Pillodar et al. [64] filled the gaps in the limitations of the results of AGB and CS by comparing the average values of AGB and CS between the actual 20 m × 20 m field plots and a 20 m resolution output data in Bacolod, Lanao del Norte. Regression analysis, with the combination of LiDAR parameters such as the canopy height model (CHM), canopy cover model (CCM), and digital terrain model (DTM), was used to model AGB and CS. On the other hand, Nesperos et al. [40] estimated the aboveground carbon stock and its CO2 equivalent using a normalized difference vegetation index (NDVI) analysis in Infantana, Quezon using Landsat 7 ETM, Landsat 5 TM, and Landsat 8 OLI/TIRS. With the use of the said dataset, they found that over the 20-year study period, dense mangrove cover had changed by −11.7%. These studies only showed that with the use of proper methodology, regardless of data resolution, mangrove biophysical factor estimates can still be achievable using RS.

3.5. Sensor Types and Performance

Table 3 shows the RS sensors being used and their performance in different mangrove resource mapping approaches. It has also been found that it is possible that by combining two or more low-resolution datasets from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, respectively, higher overall accuracy can be achieved. However, the method being used can still influence the performance of the process. As shown in Table 3, an accuracy of 77% from LiDAR data under the extent mapping (EM) objective was found. The paper intended to classify mangroves at the species-specific level [74], in which averaged values were compared to the results of [28]. Nonetheless, the results still showed that data sources with a high spatial resolution can produce higher accuracy (i.e., LiDAR and Worldview-1) compared to RS data sources with low (Landsat 5 TM and Landsat 7 ETM+) to medium (Sentinel-2) spatial resolution [28].
Figure 7 shows how the selection of sensors impacts the overall accuracy. The box-and-whisker plot is used to summarize the differences in accuracy across different sensors. The results also show a significant difference (p < 0.005) between the accuracy of sensors used in mangrove ecosystems’ mapping. The overall accuracy of the various sensor types all achieved results greater than 80%. The highest overall accuracy was achieved by LiDAR data and the lowest overall accuracy by the Sentinel-1 data only.

3.6. Mangrove Resource Mapping Methods

Table 4 below shows that starting in 2015, object-based image classification approaches using image segmentation, such as object-based image analysis (OBIA), have been frequently employed for mapping mangrove extent, because of their capacity to define objects from imagery through segmentation and classifying them with class attributes, but have numerous challenges in processing very large datasets. On the other hand, support vector machine (SVM) and nearest neighbor (NN) can also have a high accuracy in discriminating mangroves from other land cover [78,84]; in most cases, a hybrid of these algorithms with a high-resolution dataset can provide higher accuracy than other pixel-based approaches for detecting mangrove species communities [93,94,95]. However, using data with low to medium resolution can still acquire high accuracy values with the appropriate algorithm used in mangrove-extent mapping. Long and Giri [16] used ISODATA clustering, an unsupervised classification technique that was being used to classify 61 Landsat images with a 97% accuracy and a kappa coefficient of 0.926. Other algorithms used in low-resolution datasets with notable accuracy values are decision trees, random forest (RF), maximum likelihood classification (MLC), and SVM for Landsat 5 TM and Landsat 7 ETM+, whereas mangrove vegetation index (MVI), artificial neural network (ANN), MLC, and XGboost have been used for medium-resolution satellite imagery (i.e., Sentinel-2). These algorithms can provide an accuracy of 90–97% regardless of the resolution of the data. Figure 7 shows the summary of sensors commonly used and their overall accuracy. A context-based algorithm was performed by using two LiDAR derivatives during the processing: the digital terrain model (DTM) and canopy height model (CHM). These derivatives were generated using LASTools. By utilizing high-resolution data from the LiDAR sensor, this method achieved an overall accuracy of 93% [71]. Figure 8 shows the workflow Graciosa et al. (2015) developed to extract mangrove patches from inter-tidal zones.
The traditional approach to field biomass estimation of mangroves is limited by the spatial constraints of data collection and the inaccessibility of mangrove stands. When healthy, Philippine mangroves (national average of 624 Mg C ha−1) may sequester 180 teragrams (Tg) of carbon (1 Tg = 1,000,000 Mg), which equates to roughly 661 Tg of avoided CO2 emissions [97,98,99]. Mangrove forests are one of the planet’s most carbon-dense ecosystems, and blue carbon ecosystems have been identified and researched as part of climate change mitigation initiatives focused on reducing human greenhouse gas emissions [100,101,102]. Therefore, information on aboveground biomass (AGB) and carbon stock (CS) was required in national carbon accounting and for carbon credit schemes that pay for forest CS to incentivize their conservation. A common non-destructive approach is the use of allometric equations derived from parameters such as diameter at breast height (DBH) [22,23,25,103,104,105]. RS serves as a non-destructive alternative for a more robust, continuous, and spatially explicit biomass assessment [106]. Table 5 below shows the different algorithms used in estimating the AGB and CS of mangroves. Among the various kinds of algorithms used, the semi-automatic classification plugin (SCP) has provided the highest accuracy, followed by the ANN. Other methods such as RF, WEKA, and context-bdased algorithms ranged from 18 to 78%.

4. Discussion

This systematic review demonstrates that there are several mangrove resource mapping studies in the Philippines using RS data tools. Generally, the main purpose of this study was to find an optimum dataset and algorithm available and used by researchers in the Philippines.

4.1. Research Status of Mangrove Classification and RS in the Philippines

From 1979 to 2014, very few peer-reviewed mangrove classification studies using RS were conducted in the Philippines. Out of seven articles, four were published in refereed journals while the other three were printed in conference proceedings. In the period 2015–2022, after the two LiDAR projects were launched [106], interest in this topic increased, with 17 studies published in refereed journals while 35 others were reported in conference proceedings. These numbers show that there is a growing research interest in mangrove resource mapping using RS in the country. However, to date, there exists a notable challenge in the field of RS, given that most of the published articles came from only one institution (Table 7).
As a matter of fact, most of the funded projects on the mapping of mangroves using RS have been awarded to this institution. It therefore follows that the number of publications is skewed toward the institutions named. Moreover, results have also indicated that high-resolution datasets are difficult to access unless they are funded externally (Figure 9).

4.2. Sensor Types in Mangrove Resource Mapping

Vegetation distribution mapping is a traditional and essential task of RS. According to the results, mangrove distribution mapping was performed and phased into two stages: mangrove species mapping and extent mapping using time-series data for change detection. Historically, extent mapping and change detection of both mangroves and terrestrial forests was conducted using Landsat 1, Landsat 2 [41], SPOT-4 [43], Worldview-1, and ASTER [42,46]. These sensors were the ones commonly available at the time, which is also congruent with the studies of Gao [107] and Green et al. [108] in mapping mangrove extent during the period from 1990 to 2000. The next generation of well-known sensors used in mangrove resource mapping included LiDAR, MODIS, ALOS/PALSAR, Sentinel, SAR, SRTM, RapidEye, PlanetScope, and ASTER [39,42,51,56,61,63,90]. Since then, and until now, satellite-based platforms are still commonly used in mangrove resource mapping. As a matter of fact, spaceborne RS platforms such as Landsat, aside from being made available since the 1970s and until now, have also been open to the public for free. Additionally, data from these platforms, specifically optical imagery (e.g., Landsat and Sentinel) requires less pre-processing than other data types, such as radar or LiDAR, thus requiring less technical expertise to be applied. Results from previous studies also show that these satellite-based platforms are commonly used until this day because they are best suited for applications on a national or regional scale [109,110,111]. More importantly, processing software packages for optical imagery are more widely available than SAR or LiDAR processing software, regardless of either commercial or open-access source. Despite being categorized as low-resolution data, Landsat and Sentinel can still achieve an overall accuracy ranging from 95% to 97% with the use of the proper algorithms. However, in mapping mangroves at the species level, the airborne (LiDAR) RS platform has the upper hand because of its high spatial resolution. Previous results using a meta-analysis conducted by Shen et al. [28] also prove that a spatial resolution closer to the size of the plant canopy has higher overall accuracy results. In contrast, medium to low-resolution data (e.g., Landsat 8 OLI, Landsat 7 ETM+, and Landsat 5 TM) contain mixed pixels that may provide information about tree species other than the ones desired in a single pixel. Thus, medium to low-resolution data may present difficulties in discriminating mangroves to species level due to the complexity of their communities.

4.3. Different Approaches in Discriminating Mangroves from Other Land Cover

Several classic machine learning algorithms such as decision trees (DT) are commonly used in other ecosystems but rarely used in classifying mangrove ecosystems. Long et al. [44] have achieved an overall accuracy of 93% in using Landsat images for mangrove forests using decision tree classification only. Researchers in mangrove RS tend to use SVM [80,88], MLC [75], RF [81], and ANN [53], among others. Since these classifiers are non-parametric, they do not need the requirements for normality. This can be essentially beneficial when various sources of input data such as spectral, geometrical, textural, and vegetation indices are incorporated into the classification scheme to improve its overall accuracy of classification. The study clarified that SVM has shown to be more effective (99%) in discriminating mangroves from other land cover. This algorithm is particularly promising when used in mangrove classification using high-resolution datasets such as LiDAR derivatives [79,86,87] compared to the results of Songcuan et al. [88] and Gevaña et al. [77] (92–95%) using low-resolution datasets. This study supports the findings of Pham et al. [30], which showed that among the machine learning algorithms, SVM produced higher overall accuracy in both cases. For lower-resolution datasets such as Landsat images (5 TM, 7 ETM+, and 8 OLI/TIRS), algorithms such as ISODATA, DT, and SVM can still be useful in mangrove discrimination, as they give an overall accuracy ranging from 92 to 97% [16,44,66,77]. On the other hand, Baloloy et al. [50] introduced MVI, that allows the discrimination of mangroves from other land cover using medium or low-resolution datasets. By integrating MVI into other machine learning algorithms (ANN, MLC, and XGBoost), it can still obtain an accuracy from 92% to 95% without the need for data-intensive methodologies [51].
In mapping the effects of oil spills on mangroves through time (e.g., defoliation, eventual recovery through to mass mortality), SVM-RBF (97% accuracy) performed well among the other algorithms. Other classifiers such as maximum likelihood, Mahalanobis distance, and minimum distance have performed less well compared to the SVM-RBF algorithm. This might be due to positional errors which were not accounted for in the change detection analysis. Previous studies in a meta-analysis of mangrove resource mapping using RS have not yet included the temporal effect of oil spills on mangroves. However, this must be considered for further research to quantify the impacts and recovery of mangroves considering that oil spills are one of the multiple stressors of mangrove ecosystems [27,112,113,114].

4.4. Mangrove Biomass and Carbon Stock Estimation

Measuring mangrove AGB and CS has become an important topic in the field of RS research in recent years because of its relevance to international climate negotiations designed to reduce greenhouse gas emissions associated with deforestation and forest degradation [91,105]. Therefore, information on AGB and CS is required in national carbon accounting and for carbon credit schemes that will pay for forest carbon stocks to incentivize their conservation. As a matter of fact, the United Nation’s Intergovernmental Panel for Climate Change (IPCC) requires signatory countries to report their initiatives on the reduction in the emission of greenhouses as stipulated in the Kyoto Protocol, dubbed as the nationally defined contributions (NDC) [115]. Mangroves are well known to be an important repository of carbon in their biomass and in the sediments [116,117,118]. Hence, monitoring AGB and CS in mangrove ecosystems through remotely sensed data will improve throughput at any given time, and require less labor and costs. It has been observed that there are several methods that have been conducted using different RS approaches to estimate different biophysical parameters of mangroves, which can be classified into three types: biomass estimation, carbon stock estimation, and LAI inversion. Three different datasets were observed to have been used in estimating AGB and CS: optical, SAR, and LiDAR data. As observed by Baloloy et al. [60], the efficiency of all satellite data as biomass predictors are relatively higher with the use of vegetation indices. Accordingly, this is driven by the potential of the vegetation indices to highlight plant intrinsic properties that are well related to biomass accumulation, such as leaf greenness and vigor. Castillo et al. [73] have also developed a model utilizing Sentinel-2 imagery for the retrieval and predictive mapping of AGB in mangroves. Despite a few drawbacks in using optical data, such as canopy complexity and lack of SWIR data, as mentioned by Pham et al. [31] in their meta-analysis study that cited both of the aforementioned studies [60,73], which resulted in high overall accuracies of 0.92 and 0.83, respectively, and highlighted the works as revolutionary and providing new opportunities for estimating mangroves in the tropics. Airborne LiDAR has the advantage when it comes to biomass estimation since it can characterize both horizontal and vertical canopy structures. A previous study, by Pillodar et al. [64], in their estimation of AGB and CS provided a very promising result, with an overall accuracy of 0.99.
Moreover, SAR also has unique advantages in AGB and CS estimation as it is independent of the light intensity and sun illumination angle. Several studies [48,49,73] have promising results, however, there are still limitations in the use of SAR, such as foliage in forest canopies attenuating the backscatter produced by the double bounce response such that the radar wave may not penetrate deeper into the canopy due to the size of the wavelength and the high moisture conditions [49]. The same common problem was experienced by other studies that examine the influences on the accuracy of SAR image classification and AGB retrieval [109,118,119]. This backscattering response problem should be cautiously evaluated to solve limitations in future studies.

5. Conclusions

RS is an important tool for characterizing mangroves and their structure and estimating the benefits they provide due to their large coverage and frequent inaccessibility for field research. A careful review of 59 indexed research papers published in the last decades reveals the recent trend in mangrove mapping using remotely sensed data in the Philippines. The review focused on recent developments in RS approaches for mapping mangrove extent, characterizing biophysical parameters, and estimating biomass and carbon stock. After a thorough analysis, several conclusions can be drawn from these data:
  • RS in the Philippines started in 1979, just when remotely sensed data started to be used in mangrove resource mapping in the early 1970s. In 2015, research on the same topic was boosted after the UP-DREAM LiDAR project was implemented.
  • As an archipelagic country, there are still many areas in the Philippines that need to be assessed and can be subjected to future RS studies in mangroves.
  • Institutions that could find or are supported by external funders are more likely to publish studies in mangrove resource mapping using RS, especially in acquiring high-resolution datasets.
  • Medium to low-resolution spaceborne satellite (e.g., Landsat 1 and 2, Sentinel-1 and 2, and SPOT-4) data are still commonly used in mangrove resource mapping. This is because, aside from the fact that spaceborne RS platforms have been available since the 1970s, it has also been made available to the public for free, with less pre-processing and less technical expertise required.
  • Among the various machine learning approaches used in mangrove ecosystem discrimination, SVM has generally been shown to be the most effective in mangrove-extent mapping, particularly when LiDAR and other high-resolution datasets are being used. However, despite processing medium to low-resolution datasets, promising results can still be achieved using proper algorithms.
  • In mapping mangroves at the species level, the airborne (LiDAR) RS platform has the upper hand. This is because a spatial resolution closer to the size of the plant canopy has higher overall accuracy results. MVI has proven to be effective in discriminating mangroves from other land cover even using different medium to low-resolution datasets.
  • The efficiency of optical data as a biomass predictor is relatively higher with the use of vegetation indices, because it is driven by the potential of the vegetation indices to highlight plant intrinsic properties that are well related to biomass vigor. The utilization of optical data can still achieve promising results by using the newly introduced biomass predictive model.
In this study, there are a few problems that need to be addressed while analyzing the data. These issues are the following:
  • Although very high-resolution data can improve accuracy in mangrove-extent mapping, AGB estimation, and CS estimation, the cost of data acquisition and massive data storage requirements are significant drawbacks that limit the application at large scales. This explains why most published research papers used low to medium-resolution data for classification and estimation.
  • Low to medium-resolution data can be challenging when being used for species-level identification due to the complexity of canopy overlap.
  • The inaccessibility of higher-resolution datasets hinders other researchers in exploring other opportunities in mangrove resource mapping.
  • In utilizing SAR data, foliage in the forest canopies attenuates the backscatter produced by the double bounce response, such that the radar wave may not penetrate deeper into the canopy due to the size of the wavelength and the high moisture conditions.
Future directions and opportunities in mangroves and RS studies in the Philippines can address the gaps observed in the data extracted from this study. Some recommendations are the following:
  • Quantification of the impacts and recovery of mangroves influenced by oil spills, considering that these are among the multiple stressors of mangrove ecosystems.
  • The design and implementation of novel machine learning algorithms for monitoring mangrove ecosystems in the context of blue carbon programs must be put into consideration.
  • The publication of RS data from the country’s very own satellite images can be of great help to achieve better results in mapping the extent and biophysical characteristics of mangroves.
  • The need for a robust comparative analysis between remotely sensed mangrove monitoring such as distribution mapping, species-level classification, and biophysical characterization and field collected data (ground truthing) to achieve a better model is vital.
  • In the near future, an increasing trend in using different platforms, such as cloud computing, is anticipated in monitoring mangrove ecosystems at a larger scale. Cloud computing platforms such as Google Earth Engine can do most of the pre-processing phase of RS datasets at larger scales. Through this, a thorough monitoring of mangrove forest change on a regional or national scale, as well as its biophysical characteristics, is attainable.

Author Contributions

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

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

The authors would like to thank the MSU-IIT Environmental Science Program for their kind support to realize this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Afonso, F.; Félix, P.M.; Chainho, P.; Heumüller, J.A.; de Lima, R.F.; Ribeiro, F.; Brito, A.C. Assessing Ecosystem Services in Mangroves: Insights from São Tomé Island (Central Africa). Front. Environ. Sci. 2021, 9, 501673. [Google Scholar] [CrossRef]
  2. Friess, D.A.; Rogers, K.; Lovelock, C.E.; Krauss, K.W.; Hamilton, S.E.; Lee, S.Y.; Lucas, R.; Primavera, J.; Rajkaran, A.; Shi, S. The State of the World’s Mangrove Forests: Past, Present, and Future. Annu. Rev. Environ. Resour. 2019, 44, 89–115. [Google Scholar] [CrossRef]
  3. Menéndez, P.; Losada, I.J.; Beck, M.; Torres-Ortega, S.; Espejo, A.; Narayan, S.; Díaz-Simal, P.; Lange, G.-M. Valuing the protection services of mangroves at national scale: The Philippines. Ecosyst. Serv. 2018, 34, 24–36. [Google Scholar] [CrossRef]
  4. Himes-Cornell, A.; Grose, S.O.; Pendleton, L. Mangrove Ecosystem Service Values and Methodological Approaches to Valuation: Where Do We Stand? Front. Mar. Sci. 2018, 5, 376. [Google Scholar] [CrossRef]
  5. Spalding, M.; Parrett, C.L. Global patterns in mangrove recreation and tourism. Mar. Policy 2019, 110, 103540. [Google Scholar] [CrossRef]
  6. Murdiyarso, D.; Purbopuspito, J.; Kauffman, J.B.; Warren, M.W.; Sasmito, S.D.; Donato, D.C.; Manuri, S.; Krisnawati, H.; Taberima, S.; Kurnianto, S. The potential of Indonesian mangrove forests for global climate change mitigation. Nat. Clim. Chang. 2015, 5, 1089–1092. [Google Scholar] [CrossRef]
  7. Lukman, K.M.; Uchiyama, Y.; Quevedo, J.M.D.; Harding, D.; Kohsaka, R. Land Use Changes Assessment using a triangulated framework: Perception Interviews, Land-Use/Land Cover Observation, and Spatial Planning Analysis in Tanjung Batu and Derawan Island, Indonesia. Hum. Ecol. 2021, 49, 551–564. [Google Scholar] [CrossRef]
  8. Kh’ng, X.Y.; Teh, S.Y.; Koh, H.L. Modeling the mitigation effect of coastal forests on tsunami. InAIP Conf. Proc. 2017, 1870, 040009. [Google Scholar] [CrossRef]
  9. Onyena, A.P.; Sam, K. A review of the threat of oil exploitation to mangrove ecosystem: Insights from Niger Delta, Nigeria. Glob. Ecol. Conserv. 2020, 22, e00961. [Google Scholar] [CrossRef]
  10. Macreadie, P.I.; Costa, M.D.P.; Atwood, T.B.; Friess, D.A.; Kelleway, J.J.; Kennedy, H.; Lovelock, C.E.; Serrano, O.; Duarte, C.M. Blue carbon as a natural climate solution. Nat. Rev. Earth Environ. 2021, 2, 826–839. [Google Scholar] [CrossRef]
  11. Akhand, A.; Watanabe, K.; Chanda, A.; Tokoro, T.; Chakraborty, K.; Moki, H.; Tanaya, T.; Ghosh, J.; Kuwae, T. Lateral carbon fluxes and CO2 evasion from a subtropical mangrove-seagrass-coral continuum. Sci. Total. Environ. 2021, 752, 142190. [Google Scholar] [CrossRef]
  12. Spalding, M. The Global Distribution and Status of Mangrove Ecosystems. In International Newsletter of Coastal Management; Intercoast Network; University of Rhode Island: Narragansett, RI, USA, 1997; pp. 20–21. [Google Scholar]
  13. Plata-Rocha, W.; Monjardin-Armenta, S.A.; Pacheco-Angulo, C.E.; Rangel-Peraza, J.G.; Franco-Ochoa, C.; Mora-Felix, Z.D. Proximate and Underlying Deforestation Causes in a Tropical Basin through Specialized Consultation and Spatial Logistic Regression Modeling. Land 2021, 10, 186. [Google Scholar] [CrossRef]
  14. Benítez-Torres, J.A.; Roé-Sosa, A.; Amábilis-Sosa, L.E. Enhancing Environmental Services in Candelaria River by Restoring Ecological Connectivity. In Water Availability and Management in Mexico; Springer: Cham, Germany, 2020; pp. 151–170. [Google Scholar] [CrossRef]
  15. Abino, A.C.; Castillo, J.A.A.; Lee, Y.J. Assessment of species diversity, biomass and carbon sequestration potential of a natural mangrove stand in Samar, the Philippines. For. Sci. Technol. 2013, 10, 2–8. [Google Scholar] [CrossRef]
  16. Long, J.B.; Giri, C. Mapping the Philippines’ Mangrove Forests Using Landsat Imagery. Sensors 2011, 11, 2972. [Google Scholar] [CrossRef]
  17. Brown, W.H.; Fischer, A.F. Philippine mangrove swamps. In Minor Products of Philippine Forests I, Bureau of Forestry Bull; Brown, W.H., Ed.; Bureau of Printing: Manila, Philippines, 1920; pp. 9–125. Available online: https://bit.ly/3D5E5BG (accessed on 15 August 2022).
  18. Adame, M.F.; Connolly, R.M.; Turschwell, M.P.; Lovelock, C.E.; Fatoyinbo, T.; Lagomasino, D.; Goldberg, L.A.; Holdorf, J.; Friess, D.A.; Sasmito, S.D.; et al. Future carbon emissions from global mangrove forest loss. Glob. Chang. Biol. 2021, 27, 2856–2866. [Google Scholar] [CrossRef]
  19. Pham, T.D.; Yoshino, K. Impacts of mangrove management systems on mangrove changes in the Northern Coast of Vietnam. Tropics 2016, 24, 141–151. [Google Scholar] [CrossRef]
  20. Duke, N.; Nagelkerken, I.; Agardy, T.; Wells, S.; van Lavieren, H. Threats and drivers of change. In The Importance of Mangroves to People: A Call to Action; van Bochove, J.W., Sullivan, E., Nakamura, T., Eds.; United Nations Environment Programme World Conservation Monitoring Centre: Cambridge, UK, 2014; pp. 69–88. Available online: https://researchonline.jcu.edu.au/35529/ (accessed on 15 August 2022).
  21. Mariano, H.; Aguilos, M.; Dagoc, F.L.; Sumalinab, B.; Amparado, R. Abandoned Fishpond Reversal to Mangrove Forest: Will the Carbon Storage Potential Match the Natural Stand 30 Years after Reforestation? Forests 2022, 13, 847. [Google Scholar] [CrossRef]
  22. Mariano, H.G.; Dagoc, F.L.S.; Espra, A.S.; Amparado, R.F., Jr. Mangrove diversity, taxonomic classification, and mor-phological characteristics of natural and reforested mangrove forests in selected municipalities of Zamboanga Del Sur, Mindanao Island, Philippines. J. Biodivers. Environ. Sci. 2019, 15, 86–99. Available online: https://bit.ly/3cZa0cr (accessed on 14 August 2022).
  23. Osing, P.K.A.S.; Jondonero, M.A.P.; Suson, P.D.; Guihawan, J.Q.; Amparado, R.F., Jr. Species composition and diversity in a natural and reforested mangrove forests in Panguil Bay, Mindanao, Philippines. J. Biodivers. Environ. Sci. 2019, 15, 88–102. Available online: https://bit.ly/3D6AjrL (accessed on 14 August 2022).
  24. Tomppo, E.; Olsson, H.; Ståhl, G.; Nilsson, M.; Hagner, O.; Katila, M. Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sens. Environ. 2008, 112, 1982–1999. [Google Scholar] [CrossRef]
  25. Maxwell, A.E.; Warner, T.A.; Fang, F. Implementation of machine-learning classification in remote sensing: An applied review. Int. J. Remote Sens. 2018, 39, 2784–2817. [Google Scholar] [CrossRef]
  26. Gu, J.; Wang, Z.; Kuen, J.; Ma, L.; Shahroudy, A.; Shuai, B.; Liu, T.; Wang, X.; Wang, G.; Cai, J.; et al. Recent advances in convolutional neural networks. Pattern Recognit. 2018, 77, 354–377. [Google Scholar] [CrossRef]
  27. Adeli, S.; Salehi, B.; Mahdianpari, M.; Quackenbush, L.J.; Brisco, B.; Tamiminia, H.; Shaw, S. Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review. Remote Sens. 2020, 12, 2190. [Google Scholar] [CrossRef]
  28. Shen, X.; Zhang, Z.; Zhai, C.; Li, R. A Meta-Analysis of the Overall Accuracy of Extent and Species of the Coastal Mangroves. J. Peking Univ. 2022, 58, 135–146. [Google Scholar] [CrossRef]
  29. Mahdianpari, M.; Granger, J.E.; Mohammadimanesh, F.; Salehi, B.; Brisco, B.; Homayouni, S.; Gill, E.; Huberty, B.; Lang, M. Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America. Remote Sens. 2020, 12, 1882. [Google Scholar] [CrossRef]
  30. Zolkos, S.; Goetz, S.; Dubayah, R. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens. Environ. 2013, 128, 289–298. [Google Scholar] [CrossRef]
  31. Pham, T.D.; Yokoya, N.; Bui, D.T.; Yoshino, K.; Friess, D.A. Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges. Remote Sens. 2019, 11, 230. [Google Scholar] [CrossRef]
  32. Vo, T.Q.; Kuenzer, C.; Oppelt, N. How remote sensing supports mangrove ecosystem service valuation: A case study in Ca Mau province, Vietnam. Ecosyst. Serv. 2015, 14, 67–75. [Google Scholar] [CrossRef]
  33. Gliner, J.A.; Morgan, G.A.; Harmon, R.J. Meta-Analysis: Formulation and Interpretation. J. Am. Acad. Child Adolesc. Psychiatry 2003, 42, 1376–1379. [Google Scholar] [CrossRef]
  34. Mengist, W.; Soromessa, T.; Legese, G. Method for conducting systematic literature review and meta-analysis for environmental science research. Methodsx 2020, 7, 100777. [Google Scholar] [CrossRef]
  35. Hammer, Ø.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological Statistics software package for education and data analysis. Palaeontol. Electron. 2001, 4, 9. Available online: http://palaeo-electronica.org/2001_1/past/issue1_01.htm (accessed on 13 August 2022).
  36. Liberati, M.; Tetzlaff, J.; Altman, D.G.; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [PubMed]
  37. Cabello, K.E.; Germentil, M.Q.; Blanco, A.C.; Macatulad, E.G.; Iii, S.G.S. Post-disaster assessment of mangrove forest recovery in lawaan-balangiga, eastern samar using ndvi time series analysis. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, 3, 243–250. [Google Scholar] [CrossRef]
  38. Jumawan, J.H.; Macandog, D.M. GIS weighted suitability analysis as decision support tool for mangrove rehabilitation in Oriental Mindoro, Philippines. J. Ecosyst. Sci. Eco-Gov. 2021, 3, 1–13. Available online: https://bit.ly/3Rv4Ow2 (accessed on 14 August 2022).
  39. Neri, M.P.; Baloloy, A.B.; Blanco, A.C. Limitation Assessment and Workflow Refinement of the Mangrove Vegetation Index (MVI)-Based Mapping Methodology Using Sentinel-2 Imagery; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Quezon City, Philippines, 2021; pp. 235–242. [Google Scholar] [CrossRef]
  40. Nesperos, V.J.; Villanueva, C.M.; Garcia, J.; Gevaña, D. Assessment of blue carbon stock of mangrove vegetation in Infanta, Quezon, Philippines. Ecosyst. Dev. J. 2021, 11, 48–60. Available online: https://bit.ly/3AX5Jye (accessed on 14 August 2022).
  41. Lachowski, H.M.; Dietrich, D.L.; Umali, R.; Aquino, E.; Basa, V. Landsat assisted forest land-cover assessment of the Philippine Islands. Photogramm. Eng. Remote Sens. 1997, 45, 1387–1391. Available online: https://bit.ly/3Bxb2pH (accessed on 15 August 2022).
  42. Batadlan, B.D.; Paringit, E.C.; Santillan, J.R.; Caparas, A.S.; Fabila, J.L. Analysis of Background Variations in Computed Spectral Vegetation Indices and Its Implications for Mapping Mangrove Forests Using Satellite Imagery. In Proceedings of the 4th ERDT Conference, Manila, Philippines, 11 September 2009. [Google Scholar] [CrossRef]
  43. Liu, D.S.; Iverson, L.R.; Brown, S. Rates and patterns of deforestation in the Philippines: Application of geographic information system analysis. For. Ecol. Manag. 1993, 57, 1–16. [Google Scholar] [CrossRef]
  44. Long, J.; Napton, D.; Giri, C.; Graesser, J. A Mapping and Monitoring Assessment of the Philippines’ Mangrove Forests from 1990 to 2010. J. Coast. Res. 2014, 294, 260–271. [Google Scholar] [CrossRef]
  45. Paw, J.N.; Diamante, D.A.D.; Robles, N.A.; Chua, T.E.; Quitos, L.N., Jr.; Cargamento, A.G.A. Site Selection for Brackishwater Aquaculture Development and Mangrove Reforestation in Lingayen Gulf, Philippines Using Geographic Information Systems. In Proceedings of the Canadian Conference on GIS, Ottawa, ON, Canada, 24–26 March 1992; Available online: https://hdl.handle.net/20.500.12348/3043 (accessed on 15 August 2022).
  46. Paringit, E.C.; Santillan, J.R. Analysis of multi-temporal remotely sensed images of the Taklong Island National Marine Reserve (TINMAR) to assess impacts of the 2006 Guimaras oil spill. In Proceedings of the 3rd Engineering Research and Development for Technology (ERDT) Conference, Mandaluyong City, Philippines, 11 September 2009; Available online: https://bit.ly/3eAwj8w (accessed on 15 August 2022).
  47. Bragais, M.A.; Magcale-Macandog, D.B.; Arizapa, J.L.; Manalo, K.M. Vulnerability Assessment Using Lidar Data in Silangsta. Rosa Subwatershed, Philippines; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Prague, Czech Republic, 2016; pp. 1397–1401. [Google Scholar] [CrossRef]
  48. Makinano-Santillan, M.; Bolastig, C.G.; Santillan, J.R. Aboveground biomass estimation of mangroves in Siargao Island, Philippines using Sentinel-1 image. In Proceedings of the 40th Asian Conference on RS (ACRS 2019), Daejeon Convention Center (DCC), Daejeon, Korea, 14–18 October 2019; Available online: https://bit.ly/3RtnHPV (accessed on 14 August 2022).
  49. Argamosa, R.J.L.; Blanco, A.; Baloloy, A.B.; Candido, C.G.; Dumalag, J.B.L.C.; Dimapilis, L.L.C.; Paringit, E. Modelling above ground biomass of mangrove forest using sentinel-1 imagery. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2018, 4, 13–20. [Google Scholar] [CrossRef]
  50. Baloloy, A.B.; Blanco, A.C.; Ana, R.R.C.S.; Nadaoka, K. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS J. Photogramm. Remote Sens. 2020, 166, 95–117. [Google Scholar] [CrossRef]
  51. Baloloy, A.B.; Blanco, A.C.; Sharma, S.; Nadaoka, K. Development of a Rapid Mangrove Zonation Mapping Workflow Using Sentinel 2-Derived Indices and Biophysical Dataset. Front. Remote Sens. 2021, 2, 730238. [Google Scholar] [CrossRef]
  52. Buitre, M.; Zhang, H.; Lin, H. The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery. Remote Sens. 2019, 11, 688. [Google Scholar] [CrossRef]
  53. Madanguit, C.J.G.; Oñez, J.P.L.; Tan, H.G.; Villanueva, M.D.; Ordaneza, J.E.; Aurelio, R.M.; Novero, A.U. Application of support vector machine (SVM) and quick unbiased efficient statistical tree (QUEST) algorithms on mangrove and agri-cultural resource mapping using LiDAR datasets. Int. J. Appl. Environ. Sci. 2017, 12, 1821–1830. Available online: https://bit.ly/3et4A9Y (accessed on 14 August 2022).
  54. Monzon, A.K.; Reyes, S.R.; Veridiano, R.K.; Tumaneng, R.; De Alban, J.D. Synergy of Optical and Sar Data for Mapping and Monitoring Mangroves; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Prague, Czech Republic, 2016; pp. 259–266. [Google Scholar] [CrossRef]
  55. Songcuan, A.J.; Baloloy, A.B.; Blanco, A.C.; David, L.T. Mangrove Forest Extent Mapping in Siargao Island, Surigao Del Norte, Philippines Using Support Vector Machine (SMM) Classification. In Proceedings of the 36th Asian Conference on RS (ACRS), Quezon City, Metro Manila, Philippines, 19–23 October 2015; Available online: https://bit.ly/3cYqa5Q (accessed on 13 August 2022).
  56. Fallarcuna, B.; Perez, G.J. Forest cover dynamics in the Philippines from Landsat-derived global forest cover dataset (2000–2012). J. Philipp. Geosci. Remote Sens. Soc. 2016, 2, 4–17. Available online: https://bit.ly/3qitLij (accessed on 13 August 2022).
  57. Marshall, A.; Bühne, H.S.T.; Bland, L.; Pettorelli, N. Assessing ecosystem collapse risk in ecosystems dominated by foundation species: The case of fringe mangroves. Ecol. Indic. 2018, 91, 128–137. [Google Scholar] [CrossRef]
  58. Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J.; Duke, N. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 2011, 20, 154–159. [Google Scholar] [CrossRef]
  59. Faelga, R.A.G.; Paringit, E.C.; Perez, G.J.P.; Argamosa, R.J.L.; Ibanez, C.A.G.; Posilero, M.A.V.; Tandoc, F.A.M.; Zaragosa, G.P. Separability and variability of Rhizophoraceae and Avicenniaceae in a natural mangrove forest using point density distribution from LiDAR data. J. Philipp. Geosci. Remote Sens. Soc. 2015, 18–25. Available online: https://bit.ly/3x4NwNU (accessed on 14 August 2022).
  60. Baloloy, A.B.; Blanco, A.; Candido, C.G.; Argamosa, R.J.L.; Dumalag, J.B.L.C.; Dimapilis, L.L.C.; Paringit, E. Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: Rapideye, planetscope and sentinel-2. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2018, 4, 29–36. [Google Scholar] [CrossRef]
  61. Castillo, J.A.A. Assessing and Mapping of Carbon in Biomass and Soil of Mangrove Forest and Competing Land Uses in the Philippines. Ph.D. Thesis, University of Southern Quensland, Queensland, Australia, 2017. Available online: https://eprints.usq.edu.au/34443/ (accessed on 13 August 2022).
  62. Darmawan, S.; Sari, D.K.; Takeuchi, W.; Wikantika, K.; Hernawati, R. Development of aboveground mangrove forests’ biomass dataset for Southeast Asia based on ALOS-PALSAR 25-m mosaic. J. Appl. Remote Sens. 2019, 13, 044519. [Google Scholar] [CrossRef]
  63. Francisco, R.R.T.; Blanco, A.C.; Manalili, M.A.G.; Gatdula, N.B.; Songcuan, A.J.G.; Landicho, K.P.C.; Reyes, R.B.; Apura, R.J.A. Mapping of Blue Carbon Ecosystems: Effect of Proximity, Activity Types and Frequency of Visits in the Accuracy of Participatory Maps; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Casablanca, Morocco, 2019; pp. 83–88. [Google Scholar] [CrossRef]
  64. Pillodar, F.O.; Mero, D.C.; Mostrales, D.S.; Astillero, S.G.; Ignacio, M.T.T. Estimation of Aboveground Biomass and Carbon Stock of Bacolod Protected Landscape and Seascape Using LiDAR Data and GIS. Int. J. Adv. Agric. Environ. Eng. 2016, 4, 1. [Google Scholar] [CrossRef]
  65. Venturillo, R.M. Spatio-temporal mapping, biomass, and carbon stock assessment of mangrove forest in Aborlan, Pa-lawan, Philippines. J. Nature Stud. 2016, 15, 90–103. Available online: https://bit.ly/3qiqi3d (accessed on 13 August 2022).
  66. Domingo, G.A.; Mallillin, M.M.; Perez, A.M.C.; Claridades, A.R.C.; Tamondong, A.M. 3D Visualization of Mangrove and Aquaculture Conversion in Banate Bay, Iloilo; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Kuala Lumpur, Malaysia, 2017; pp. 53–60. [Google Scholar] [CrossRef]
  67. Domingo, G.A.; Claridades, A.R.C.; Tupas, M.E.A. Unmanned Aerial Vehicle (UAV) Survey-Assisted 3d Mangrove Tree Modeling; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Dehradun, India, 2018; pp. 123–127. [Google Scholar] [CrossRef]
  68. Estacio, I.; Blanco, A. Simulating Species Dominance in Mixed Mangrove Forests Considering Species-Specific Responses to Shading, Salinity, and Inundation Frequency. Adv. Intell. Syst. 2020, 9, 167–183. [Google Scholar] [CrossRef]
  69. Estacio, I.; Quinton, K.; Macatulad, E.; Salmo, S. A species-specific individual-based Simulation model of mixed mangrove forest stands. In Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2019), Ateneo de Manila, Philippines, 7–9 July 2021. [Google Scholar] [CrossRef]
  70. Martinez, K.P.; Burgos, D.F.M.; Blanco, A.C.; Iii, S.G.S. Multi-sensor approach to leaf area index estimation using statistical machine learning models: A case on mangrove forests. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, 3, 109–115. [Google Scholar] [CrossRef]
  71. Graciosa, J.C.A.; Violanda, R.R.; Diola, A.G.; Porpetcho, W.P.; Dy, D.T.; Otadoy, R.E.S. A Context-Based Approach in Mangrove Patches Extraction from LiDAR Data: A Case Study in Pinamungajan, Cebu, Central Philippines. In Proceedings of the Asian Conference on RS (ACRS), Quezon City, Philippines, 19–23 October 2015; Available online: https://bit.ly/3qqs2Hq (accessed on 12 August 2022).
  72. Jalbuena, R.L.; Peralta, R.V.; Tamondong, A.M. Object-Based Image Analysis for Mangroves Extraction Using LiDAR Datasets and Orthophoto. In Proceedings of the Asian Conference on RS (ACRS), Quezon City, Philippines, 19–23 October 2015; Available online: https://bit.ly/3TU1V9k (accessed on 12 August 2022).
  73. Castillo, J.A.A.; Apan, A.A.; Maraseni, T.N.; Salmo, S.G., III. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS J. Photogramm. Remote Sens. 2017, 134, 70–85. [Google Scholar] [CrossRef]
  74. Faelga, R.A.G.; Paringit, E.C.; Perez, G.J.P.; Ibanez, C.A.G.; Argamosa, R.A.L.; Posilero, M.A.V.; Zaragosa, G.P.; Tandoc, F.A.M.; Malabanan, M.V. Mangrove Plantation Forest Assessment Using Structural Attributes Derived from Light Detection and Ranging (LIDAR) Data; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Prague, Czech Republic, 2016; pp. 617–623. [Google Scholar] [CrossRef]
  75. Gevana, D.; Camacho, L.; Carandang, A.; Camacho, S.; Im, S. Land use characterization and change detection of a small mangrove area in Banacon Island, Bohol, Philippines using a maximum likelihood classification method. For. Sci. Technol. 2015, 11, 197–205. [Google Scholar] [CrossRef]
  76. Redondo, A.F.S.; Dagoc, K.M.F.; Ignacio, M.T.T.; Sanchez, R.R.G.; Tampus, A.D. Seagrass mapping and assessment using RS in the Municipality of Kauswagan, Lanao del Norte, Philippines. J. Biodivers. Environ. Sci. 2017, 11, 74–88. Available online: https://bit.ly/3RMz45e (accessed on 13 August 2022).
  77. Songcuan, A.J.; Baloloy, A.B.; Blanco, A.C.; David, L.T. Mangrove Forest Extent Mapping in Southwestern Luzon Using 2015 Landsat Imagery. In Proceedings of the 36th Asian Conference on RS (ACRS), Quezon City, Philippines, 19–23 October 2015. [Google Scholar] [CrossRef]
  78. Alon, A.S.; Festijo, E.D.; Juanico, D.E.O. An Object-Based Supervised Nearest Neighbor Method for Extraction of Rhizophora in Mangrove Forest from LiDAR Data and Orthophoto. In Proceedings of the 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), Shah Alam, Malaysia, 7 October 2019. [Google Scholar] [CrossRef]
  79. Alon, A.S.; Festijo, E.D.; Casuat, C.D. Tree Extraction of Airborne LiDAR Data Based on Coordinates of Deep Learning Object Detection from Orthophoto over Complex Mangrove Forest. Int. J. Emerg. Trends Eng. Res. 2020, 8, 2107–2111. [Google Scholar] [CrossRef]
  80. Cabili, J.R.C.; Torres, M.A.; Ignacio, M.T.T.; Dagoc, K.M.F.; Guihawan, J.Q. Object-Based Image Analysis for Extraction of Mangrove Forests in Rehabilitated Areas of Bacolod, Lanao Del Norte, Philippines using Lidar Data and GIS. J. Appl. Sci. Res. 2018, 14, 6–10. [Google Scholar] [CrossRef]
  81. Campomanes, F.; Pada, A.; Silapan, J. Mangrove Classification Using Support Vector Machines and Random Forest Algorithm: A Comparative Study. In Proceedings of the 6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016, Enschede, Netherlands, 14–16 September 2016. [Google Scholar] [CrossRef]
  82. Cubillas, J.E.; Japitana, M. The Application of Support Vector Machine (SVM) Using Cielab Color Model, Color Intensity and Color Constancy as Features for Ortho Image Classification of Benthic Habitats in Hinatuan, Surigao Del Sur, Philippines; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Prague, Czech Republic, 2016; pp. 189–194. [Google Scholar] [CrossRef]
  83. David, L.C.; Ballado, A.J. Application of Object-Based Image Analysis and Support Vector Machine in Mapping Mangrove Forest Using LiDAR and Orthophoto: A Case Study in Calatagan, Batangas. In Proceedings of the 36th Asian Conference on RS (ACRS), Quezon City, Philippines, 19–23 October 2015. [Google Scholar] [CrossRef]
  84. David, L.C.G.; Ballado, A.H. Mapping Mangrove Forest from LiDAR Data Using Object-Based Image Analysis and Support Vector Machine: The Case of Calatagan, Batangas. In Proceedings of the 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Cebu, Philippines, 9–12 December 2015. [Google Scholar] [CrossRef]
  85. David, L.C.G.; Ballado, A.J. Object-Based Land Use and Land Cover Mapping from LiDAR Data and Orthophoto Application of Decision Tree-Based Data Selection for SVM Classification. In Proceedings of the 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dayalbagh, Agra, India, 21–23 December 2016. [Google Scholar] [CrossRef]
  86. Guihawan, J.Q.; Tabugo, S.R.M.; Amparado, R.F.; Arriesgado, O.I.; Burlat, C.S.; Ignacio, M.T.T. Mapping of Agricultural and Coastal Resources in the Municipality of Bacolod, Lanao del Norte using LiDAR datasets and GIS. In Proceedings of the International Conference on Environment and Forest Conservation, Dapitan City, Philippines, 18–21 November 2018; Available online: https://bit.ly/3qnsYwy (accessed on 13 August 2022).
  87. Japitana, M.V.; Cubillas, J.E.D. Benthic Habitat Classification and Mapping Using Support Vector Machine Algorithm in Hinatuan, Surigao del Sur, Philippines. 2015. Available online: https://bit.ly/semanticscholar2015 (accessed on 10 August 2022).
  88. Luna, D.A.; Ocampo, D.V.; Quiñones, S.G.L.; Rivera, E.C.; Sejalbo, C.G.; Zara, P.R.; Hizon, K.B.M.; Reyes, J.A.V.D.; Ticzon, V.S.; Macandog, D.M. Coastal Objects: Mangrove Area Extraction Using Remote Sensing and Aerial LiDAR Data in Roxas, Oriental Mindoro. Environ. Ecol. Res. 2017, 5, 282–288. [Google Scholar] [CrossRef]
  89. Pada, A.V.; Silapan, J.; Cabanlit, M.A.; Campomanes, F.; Garcia, J.J. Mangrove Forest Cover Extraction of the Coastal Areas of Negros Occidental, Western Visayas, Philippines Using Lidar Data; The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Prague, Czech Republic, 2016; pp. 73–79. [Google Scholar] [CrossRef]
  90. Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, N. Global Mangrove Watch: Updated 2010 Mangrove Forest Extent (v2.5). Remote Sens. 2022, 14, 1034. [Google Scholar] [CrossRef]
  91. De Alban, J.T.; Monzon, A.V.; Veridiano, R.A.; Rico, E.B.; Pales, J.C.; Tumaneng, R.D. Forest Change Detection and Biomass Estimation using ALOS/PALSAR Data in support of REDD+ Readiness Activities in Palawan, Philippines. K&C Science Report–Phase 3. 2016. Available online: https://bit.ly/3elfcHN (accessed on 12 August 2022).
  92. Conopio, M.; Baloloy, A.B.; Medina, J.; Blanco, A.C. Spatio-Temporal Mapping and Analysis of Mangrove Extents around Manila Bay Using Landsat Satellite Imagery and Mangrove Vegetation Index (MVI); The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences: Quezon City, Philippines, 2021; pp. 103–108. [Google Scholar] [CrossRef]
  93. Jia, M.; Zhang, Y.; Wang, Z.; Song, K.; Ren, C. Mapping the distribution of mangrove species in the Core Zone of Mai Po Marshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data. Int. J. Appl. Earth Obs. Geoinf. 2014, 33, 226–231. [Google Scholar] [CrossRef]
  94. Kamal, M.; Phinn, S. Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach. Remote Sens. 2011, 3, 2222–2242. [Google Scholar] [CrossRef]
  95. Kamal, M.; Phinn, S.; Johansen, K. Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets. Remote Sens. 2015, 7, 4753–4783. [Google Scholar] [CrossRef]
  96. Pagkalinawan, E.H. Mangrove Forest mapping using Landsat 8 images. In State of the Mangrove Summit: Northwestern Luzon Proceedings; Salmo, S.G., III, Favis, A.M.T., Ting, M.N.S., Eds.; Ateneo de Manila University: Quezon City, Philippines, 2015; Volume 7, pp. 60–64. [Google Scholar] [CrossRef]
  97. Quevedo, J.M.D.; Uchiyama, Y.; Kohsaka, R. Community perceptions of long-term mangrove cover changes and its drivers from a typhoon-prone province in the Philippines. AMBIO 2021, 51, 972–989. [Google Scholar] [CrossRef]
  98. Gevaña, D.T.; Camacho, L.D.; Pulhin, J.M. Conserving Mangroves for Their Blue Carbon: Insights and Prospects for Community-Based Mangrove Management in Southeast Asia. Coast. Res. Lib. 2018, 25, 579–588. [Google Scholar] [CrossRef]
  99. Salmo, S.G., III. Mangrove blue carbon in the Verde Island Passage. In Nature is 30 (Climate Strategy); Conservation International Philippines: Quezon City, Philippines, 2019; ISBN 978-971-94594-3-9. Available online: https://bit.ly/archium26 (accessed on 13 August 2022).
  100. USAID. BLEADERS [Building Low Emission Alternatives to Develop Economic Resilience and Sustainability]. Assessment of Carbon Sequestration Potential of Mangrove Forests in the Talon-talon and Mampang Treevolution Sites of Zamboanga City. United States Agency for International Development (USAID). 2017; pp. 1–57. Available online: https://pdf.usaid.gov/pdf_docs/PA00TC68.pdf (accessed on 14 August 2022).
  101. Muhd-Ekhzarizal, M.E.; Mohd-Hasmadi, I.; Hamdan, O.; Mohamad-Roslan, M.K.; Noor-Shaila, S. Estimation of aboveground biomass in mangrove forests using vegetation indices from SPOT-5 image. J. Trop. For. Sci. 2018, 30, 224–233. [Google Scholar] [CrossRef]
  102. Valenzuela, R.B.; Yeo-Chang, Y.; Park, M.S.; Chun, J.-N. Local People’s Participation in Mangrove Restoration Projects and Impacts on Social Capital and Livelihood: A Case Study in the Philippines. Forests 2020, 11, 580. [Google Scholar] [CrossRef]
  103. Zaragosa, M.J.G.; Aranico, E.C.; Tampus, A.D.; Amparado, R.F., Jr. Carbon stock assessment of three different vegetative covers in Kapatagan, Lanao del Norte, Philippines. AES Int. J. Bioflux Soc. 2016, 8, 205–220. Available online: https://bit.ly/3D77dso (accessed on 13 August 2022).
  104. Pongon, R.S.; Aranico, E.C.; Dagoc, F.L.S.; Amparado, R.F., Jr. Carbon stock assessment of bamboo plantations in Northern Mindanao, Philippines. J. Biodivers. Environ. Sci. 2016, 9, 97–112. Available online: https://bit.ly/3QsCVmG (accessed on 12 August 2022).
  105. Herold, M.; Johns, T. Linking requirements with capabilities for deforestation monitoring in the context of the UNFCCC-REDD process. Environ. Res. Lett. 2007, 2, 045025. [Google Scholar] [CrossRef]
  106. Blanco, A.; Tamondong, A.M.; Perez, A.M.C.; Ang, M.R.C.O.; Paringit, E. The Phillidar 2 Program: National RESOURCE inventory of the Philippines Using Lidar and Other Remotely Sensed Data; ISPRS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: Berlin, Germany, 2015; pp. 1123–1127. [Google Scholar] [CrossRef]
  107. Gao, J. A comparative study on spatial and spectral resolutions of satellite data in mapping mangrove forests. Int. J. Remote Sens. 1999, 20, 2823–2833. [Google Scholar] [CrossRef]
  108. Green, E.P.; Clark, C.D.; Mumby, P.J.; Edwards, A.J.; Ellis, A.C. Remote sensing techniques for mangrove mapping. Int. J. Remote Sens. 1998, 19, 935–956. [Google Scholar] [CrossRef]
  109. Wessel, M.; Brandmeier, M.; Tiede, D. Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sens. 2018, 10, 1419. [Google Scholar] [CrossRef]
  110. Hościło, A.; Lewandowska, A. Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data. Remote Sens. 2019, 11, 929. [Google Scholar] [CrossRef]
  111. Zeng, L.; Wardlow, B.D.; Xiang, D.; Hu, S.; Li, D. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sens. Environ. 2020, 237, 111511. [Google Scholar] [CrossRef]
  112. Deng, W.; Zhang, X.; Luo, J.; Peng, Y. South China Sea Environment Monitoring Using Remote Sensing Techniques. J. Coast. Res. 2020, 95, 29–33. [Google Scholar] [CrossRef]
  113. Connolly, R.M.; Connolly, F.N.; Hayes, M.A. Oil spill from the Era: Mangroves taking eons to recover. Mar. Pollut. Bull. 2020, 153, 110965. [Google Scholar] [CrossRef]
  114. Denison, S.; Forster, P.M.; Smith, C.J. Guidance on emissions metrics for nationally determined contributions under the Paris Agreement. Environ. Res. Lett. 2019, 14, 124002. [Google Scholar] [CrossRef]
  115. Gu, X.; Zhao, H.; Peng, C.; Guo, X.; Lin, Q.; Yang, Q.; Chen, L. The mangrove blue carbon sink potential: Evidence from three net primary production assessment methods. For. Ecol. Manag. 2022, 504, 119848. [Google Scholar] [CrossRef]
  116. Jennerjahn, T.C. Relevance and magnitude of ‘Blue Carbon’ storage in mangrove sediments: Carbon accumulation rates vs. stocks, sources vs. sinks. Estuar. Coast. Shelf Sci. 2020, 247, 107027. [Google Scholar] [CrossRef]
  117. Jia, P.; Huang, W.; Zhang, Z.; Cheng, J.; Xiao, Y. The Carbon Sink of Mangrove Ecological Restoration between 1988–2020 in Qinglan Bay, Hainan Island, China. Forests 2022, 13, 1547. [Google Scholar] [CrossRef]
  118. Huang, K.; Yang, G.; Yuan, Y.; Sun, W.; Meng, X.; Ge, Y. Optical and SAR images Combined Mangrove Index based on multi-feature fusion. Sci. Remote Sens. 2022, 5, 100040. [Google Scholar] [CrossRef]
  119. Maurya, K.; Mahajan, S.; Chaube, N. Remote sensing techniques: Mapping and monitoring of mangrove ecosystem—A review. Complex Intell. Syst. 2021, 7, 2797–2818. [Google Scholar] [CrossRef]
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Figure 1. Word cloud of keywords used in mangrove monitoring mapping using RS. The size of the word shows the frequency of the specific term that appeared in all articles used in this study.
Figure 1. Word cloud of keywords used in mangrove monitoring mapping using RS. The size of the word shows the frequency of the specific term that appeared in all articles used in this study.
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Forests 14 01080 g002 550
Figure 2. A modified PSALSAR flow diagram adapted from [34], in selecting relevant articles. Initially, there were 2127 articles found from different portals through the designed criteria. A total of 59 articles remained after a thorough evaluation of the articles using inclusion and exclusion criteria.
Figure 2. A modified PSALSAR flow diagram adapted from [34], in selecting relevant articles. Initially, there were 2127 articles found from different portals through the designed criteria. A total of 59 articles remained after a thorough evaluation of the articles using inclusion and exclusion criteria.
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Forests 14 01080 g003 550
Figure 3. The trend of published articles in the past three decades highlights the number of studies each year. Years without published articles related to mangroves and RS are removed.
Figure 3. The trend of published articles in the past three decades highlights the number of studies each year. Years without published articles related to mangroves and RS are removed.
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Forests 14 01080 g004 550
Figure 4. Funding agencies that were involved in the realization of the published articles related to mangroves and RS.
Figure 4. Funding agencies that were involved in the realization of the published articles related to mangroves and RS.
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Figure 5. A geographical map of the Philippines illustrates the frequency of published articles conducted per province.
Figure 5. A geographical map of the Philippines illustrates the frequency of published articles conducted per province.
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Figure 6. The evolution of mangrove ecosystem mapping using RS since 1979. Yellow, purple, and green boxes represent studies on mangrove forest and species mapping, mangrove-extent mapping using time-series data, and mangrove attributes, respectively.
Figure 6. The evolution of mangrove ecosystem mapping using RS since 1979. Yellow, purple, and green boxes represent studies on mangrove forest and species mapping, mangrove-extent mapping using time-series data, and mangrove attributes, respectively.
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Figure 7. Box-and-whisker plot illustrating the effect of sensors on overall accuracy. Box limits show the 25 and 75 percentiles; whiskers show the minimum and maximum values (except for outliers in LiDAR and Landsat 8 OLI/TIRS); lines inside the boxes represent the median. Sensors are recorded as they were used in the existing studies (combined or solo).
Figure 7. Box-and-whisker plot illustrating the effect of sensors on overall accuracy. Box limits show the 25 and 75 percentiles; whiskers show the minimum and maximum values (except for outliers in LiDAR and Landsat 8 OLI/TIRS); lines inside the boxes represent the median. Sensors are recorded as they were used in the existing studies (combined or solo).
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Figure 8. A workflow developed by Graciosa et al. [71] for extracting mangrove patches.
Figure 8. A workflow developed by Graciosa et al. [71] for extracting mangrove patches.
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Figure 9. The number of published articles (n) and their RS dataset under different funding sources.
Figure 9. The number of published articles (n) and their RS dataset under different funding sources.
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Table
Table 1. Attributes extracted from the included articles for meta-analysis. Data are collected and consolidated in a spreadsheet for further analysis.
Table 1. Attributes extracted from the included articles for meta-analysis. Data are collected and consolidated in a spreadsheet for further analysis.
NumberAttributeDescription
1Title—
2Author(s)—
3Year—
4Keywords—
5PublisherJournal Name
6Study AreaProvinces
7Spatial ResolutionMeters
8Frequency 1RGB bands, NIR, SWIR1, SWIR2, etc.
9SensorRS sensors
10Classifier 1SVM, KNN, MLC, etc.
11ObjectiveExtent mapping, AGB, CS, etc.
12PerformancePercentage
13Funding Agencies—
1 Near infrared (NIR); shortwave infrared (SWIR); support vector machine (SVM); K-nearest neighbor (KNN); maximum likelihood classification (MLC).
Table
Table 2. Journal and conferences each year where articles related to mangroves and RS were published. Years without published articles related to mangroves and RS are removed.
Table 2. Journal and conferences each year where articles related to mangroves and RS were published. Years without published articles related to mangroves and RS are removed.
YearJournal/Conference
1979Photogrammetric Engineering and Remote Sensing
1992Canadian Conference on Remote Sensing
1993Forest Ecology and Management
2009ERDT Conference
2011Sensors
2014Journal on Coastal Research
2015Asian Conference on Remote Sensing; School Journal
2016HNICEM; ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences; Journal of the Philippine Geosciences and Remote Sensing Society; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; Journal of Nature Studies
2017USQ; ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences; Environment and Ecology Research; International Journal of Applied Environmental Science; International Journal of Advances In Agricultural and Environmental Engineering; American Journal of Environment and Climate
2018ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences; Journal of Applied Sciences Research; Ecological Indicators
2019International Conference on Sytems Engineering and Technology; Remote Sensing; ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences; Journal of Applied Remote Sensing; SIMULTECH; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; ACRS
2020International Journal of Emerging Trends in Engineering Research; ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences; Publiscience; IEEE
2021Frontiers in Remote Sensing; ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences; Journal of Ecosystem Science and Eco-Governance; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; Ecosystems and Development Journal; Ambio
2022Remote Sensing
Table
Table 3. RS sensors and their performance in different mangrove resource mapping approaches such as extent mapping (EM), 3D visualization (3DV), aboveground biomass and carbon stock assessment (AGBCS), change detection (CD), leaf area index (LAI), and oil-spill impact (OSI).
Table 3. RS sensors and their performance in different mangrove resource mapping approaches such as extent mapping (EM), 3D visualization (3DV), aboveground biomass and carbon stock assessment (AGBCS), change detection (CD), leaf area index (LAI), and oil-spill impact (OSI).
SensorsAccuracyObjectiveReferences
ASTER75%EM[42]
Landsat 5 TM81%EM[75]
Landsat 5 TM; Landsat 7 ETM+; MODIS; SPOT-487–92%EM[56]
Landsat 7 ETM+; Landsat 5 TM96%EM[42]
Landsat 7 ETM+; Landsat 8 OLI/TIRS97%EM[16]
Landsat 8 OLI/TIRS90–95%EM[55,57,75,76]
LiDAR77–99%EM[53,71,72,74,77,78,79,80,81,82,83,84,85,86,87,88,89]
Sentinel-295%EM[39,51,90]
Sentinel-2; Landsat 8 OLI/TIRS92%EM[50]
Landsat 1; Landsat 2; Landsat 7 ETM+; Landsat 5 TM; Landsat 8 OLI/TIRS93%3DV[66,67]
LiDAR99%AGBCS[64]
ALOS-PALSAR82%AGBCS[62]
Sentinel-159–78%AGBCS[48,49]
Sentinel-1; Sentinel-283%AGBCS[73]
Sentinel-2; RapidEye; PlanetScope92%AGBCS[60]
SRTM/SAR86%AGBCS[61]
Landsat 5 TM; Landsat 7 ETM+; Landsat 8 OLI/TIRS96%AGBCS[40]
PlanetScope; Sentinel-268%AGBCS[63]
ALOS-PALSAR89%CD[91]
Landsat 1; Landsat 290%CD[41]
Landsat 5 TM; Landsat 7 ETM+; Landsat 8 OLI/TIRS92%CD[92]
Landsat 8 OLI/TIRS60–92%CD[32,52]
Sentinel-289%LAI[70]
ASTER; Worldview-197%OSI[48]
Table
Table 4. Sensors and algorithms used in mangrove-extent mapping and their corresponding accuracy.
Table 4. Sensors and algorithms used in mangrove-extent mapping and their corresponding accuracy.
SensorsAlgorithm *AccuracyReferences
Landsat 7 ETM+; Landsat 8 OLI/TIRSISODATA97%[16]
ASTERVegetation Indices75%[42]
Landsat 7 ETM+; Landsat 5 TMDecision Trees96%[44]
Sentinel-2; Landsat 8 OLI/TIRSMVI92%[50]
Sentinel-2ANN; MLC95%[51]
XGBoost95%[90]
LiDARSVM; QUEST97%[53]
SVM99%[87,89]
OBIA; SVM; NN91–98%[70,80]
Context-based algorithm (see Figure 8)93%[71]
MDC77%[74]
OBIA; NN99%[78]
SVM; RF98%[81]
OBIA; SVM90–98%[82,83,84,88]
OBIA94%[86]
Decision Tree; SVM89%[85]
Landsat 8 OLI/TIRSSVM92–95%[55,77]
RF92%[57]
MLC90%[76]
Landsat 5 TMMLC81%[75]
Landsat 8 OLI/TIRSISODATA82%[96]
* Object-based image analysis (OBIA); support vector machine (SVM); nearest neighbor (NN); random forest (RF); quick unbiased efficient statistical tree (QUEST); Mahalanobis distance classifier (MDC); maximum likelihood classification (MLC); mangrove vegetation index (MVI); artificial neural network (ANN).
Table
Table 5. Methods used in aboveground biomass and carbon stock estimation in mangrove ecosystems.
Table 5. Methods used in aboveground biomass and carbon stock estimation in mangrove ecosystems.
SensorsAlgorithm *AccuracyReferences
Sentinel-1RF78%[48,49]
Sentinel-2; RapidEye; PlanetScopeANN92%[60]
Sentinel-1 SAR; Sentinel-2WEKA ML algorithms (see Table 6)83–86%[61,73]
ALOS-PALSARRule-based algorithm82%[62]
Landsat 5 TM; Landsat 7 ETM+; Landsat 8 OLI/TIRSSCP96%[40]
* Random forest (RF); artificial neural network (ANN); semi-automatic classification plugin (SCP); Waikato environment for knowledge analysis (WEKA).
Table
Table 6. Algorithms available from WEKA machine learning software [61].
Table 6. Algorithms available from WEKA machine learning software [61].
AlgorithmClassifier TypeKey Description
ElasticNetFunctionsCoordinate-descent-based regression for elastic-net-related problem
GaussianProcessesFunctionsGaussian processes for regression
IsotonicRegressionFunctionsLearns an isotonic regression model
LeastMedSqFunctionsLeast median squared linear regression
MultilayerPerceptronFunctionsBackpropagation to classify instances
PaceRegressionFunctionsPace regression linear models
RBFNetworkFunctionsNormalized Gaussian radial basis function network
RBFRegressorFunctionsSupervised radial basis function networks
SMOregFunctionsSupport vector machine for regression
AlternatingModelTreeTreesAn alternating model tree by minimizing squared error
DecisionStumpTreesBuilding and using a decision stump
RandomForestTreesConstruction a forest of random trees
RandomTreeTreesTree construction based on K randomly chosen attributes
REPTreeTreesFast decision tree learner
IBkLazyK-nearest neighbor classifier
KStarLazyInstance-based classifier
LWLLazyLocally weighted learning
Table
Table 7. Funding sources of institutions that conducted mangrove resource mapping using RS.
Table 7. Funding sources of institutions that conducted mangrove resource mapping using RS.
InstitutionExternally FundedInternally FundedOther Funding Source
University of the Philippines Cebu2
Ateneo de Manila University1
Caraga State University-Butuan3 1
DENR1
International711
Mapúa Institute of Technology3
Mindanao State University-Iligan Institute of Technology3
Mindanao State University-Marawi City1
NAMRIA1
NEDA1
Philippine Science High School-Western Visayas Campus 1
Technological Institute of the Philippines2
University of San Carlos-Cebu1
University of the Philippines Diliman1912
University of the Philippines Los Baños2
University of the Philippines Mindanao 1
Others2
Total4926
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Pillodar, F.; Suson, P.; Aguilos, M.; Amparado, R., Jr. Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis. Forests 2023, 14, 1080. https://doi.org/10.3390/f14061080

AMA Style

Pillodar F, Suson P, Aguilos M, Amparado R Jr. Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis. Forests. 2023; 14(6):1080. https://doi.org/10.3390/f14061080

Chicago/Turabian Style

Pillodar, Fejaycris, Peter Suson, Maricar Aguilos, and Ruben Amparado, Jr. 2023. "Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis" Forests 14, no. 6: 1080. https://doi.org/10.3390/f14061080

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