1. Introduction
Mangrove ecosystems are among the most productive ecosystems that exist along coastal areas in tropical and sub-tropical regions. These ecosystems provide unique ecological and environmental benefits including coastal protection (i.e., against floods and wave attenuation) [
1,
2], carbon sequestration [
3,
4], pollution and waste abatement [
5,
6], and pharmaceutical production [
7,
8]. Additionally, mangrove ecosystems are important habitats for various fauna, providing valuable food services for shrimp farming and fishery [
9,
10]. However, mangrove loss (i.e., in species and extent) due to the fact of anthropogenic activities, catastrophic natural hazards in coastal areas, and climate change continued in recent decades has led to severe environmental degradation [
11,
12,
13,
14]. Accordingly, it is a global, regional, and local concern to accurately map these valuable ecosystems to prevent their loss and establish effective practices for their sustainable management.
In the current era, the advancement of remote sensing technology has created an unprecedented opportunity to study various natural resources such as mangrove communities [
15,
16,
17,
18,
19,
20]. In particular, remote sensing systems provide frequent and accurate data sets over mangrove communities with spatial consistency and synoptic views. These capabilities make remote sensing an appealing choice for mangrove studies compared to conventional approaches that rely on in situ data collection. This is rooted in the fact that conventional practices are time consuming, resource intensive, and, on some occasions, infeasible (i.e., due to the limited access and harsh environment of mangrove communities or large-scale studies) [
21,
22].
Remote sensing data sets have different characteristics in terms of electromagnetic spectrum domains, spatial resolutions, temporal resolutions, and radiometric resolutions. In particular, multi-spectral, synthetic aperture radar (SAR), light detection and ranging (LiDAR), and hyperspectral data are common remote sensing resources that have been employed either individually or in conjunction for mangrove studies [
23,
24,
25,
26,
27,
28]. For instance, Ashiagbor et al. [
29] examined the capability of the Sentinel-1 SAR data to obtain information about mangroves in the Keta Lagoon Complex Ramsar Site (KLCRS) to support sustainable conservation and restoration. Likewise, Bindu et al. [
30] employed multi-spectral images, in situ data, and allometric equations [
31] to derive the above-ground biomass of mangroves and then converted the estimated values to carbon content. Moreover, Hu et al. [
32] incorporated LiDAR, multi-spectral, topographical, and climate data to estimate the above-ground biomass density of mangrove communities. In another study, Lucas et al. [
33] integrated time-series multi-spectral and SAR data to estimate mangroves’ age in Matang Mangrove Forest Reserve (MMFR), Malaysia. Later, interferometric SAR data were combined with very high-resolution stereo images to estimate the canopy height of mangroves [
33].
Along with remote sensing data, machine learning algorithms have been extensively employed to exploit the full potential of these data for automated mapping of mangroves. In this respect, different machine learning algorithms, including maximum likelihood [
34], support vector machine (SVM) [
35], random forest (RF) [
36,
37], K nearest neighbor (KNN) [
38], classification and regression trees (CART) [
39], and artificial neural networks (ANNs) [
40] have been utilized. For example, Parida and Kumar [
35] implemented an SVM algorithm to map mangrove extent between 2009 and 2019 using Landsat-5 and Sentinel-2 data sets. Their results indicated an increase in the spatial extent of mangroves in the Odisha coast, which were mainly associated with plantation, awareness, restoration, and management. Moreover, Behera et al. [
37] applied an RF algorithm for mangrove mapping in Bhitarkanika Wildlife Sanctuary, India. To this end, red-edge spectral bands and chlorophyll absorption information of AVIRIS-NG and Sentinel-2 images were employed, and the results indicated the preeminence of Sentinel-2 images for this task. Likewise, Zhang et al. [
41] used multi-temporal Landsat-5 and digital elevation model (DEM) data to map mangrove forests based on a decision tree algorithm. It was reported that employing multi-temporal data can efficiently enhance the classification results by reducing the tidal effect, and the decision tree approach was superior to conventional statistical classifiers. In another study, Bihamta Toosi et al. [
42] compared the performances of four machine learning algorithms (i.e., linear SVM, radial SVM, RF, and regularization in discriminant analysis) for mangrove classification and change detection using Landsat archives. The k-fold cross-section validation step was executed, and it was reported that the RF algorithm achieved the best performance.
Previous studies have acknowledged the benefits of multi-source remote sensing data and different machine learning algorithms for mangrove classification [
41,
43,
44,
45,
46]. Meanwhile, few studies have been conducted to generate mangrove maps using multi-spectral data and the ANN algorithm [
40,
47,
48,
49], and its practicability is underreported [
40]. Nevertheless, the applicability of the ANN algorithm for mangrove ecosystem mapping using multi-temporal and multi-source remote sensing data has not been comprehensively explored.
Notwithstanding the foregoing, this study aimed to investigate the potential of combining the ANN algorithm and multi-source (i.e., multi-spectral and SAR) remote sensing data for mangrove ecosystem mapping. In this regard, the ANN algorithms with different topologies and specifications were implemented for mangrove ecosystem mapping. In particular, the effects of the number of layers and neurons, learning algorithms, type of activation functions, and learning rates for small-to-medium-sized ANN models were investigated. Subsequently, several other analyses were conducted to explore the impact of data transformation/standardization, a limited number of training samples, noise labels, as well as multi-temporal and multi-source remote sensing data sets on the classification accuracy using the ANN algorithm.
5. Discussion
In this section, first, a few general remarks are provided. Then, three different analyses are presented to provide a more comprehensive overview of the performance of the ANN models. Although the input data in these analyses were related to the mangrove ecosystem, the reached implications primarily manifest the behavior of ANN models that could elucidate a path for readers implementing ANN algorithms over different study areas.
5.1. General Remarks
Mangrove ecosystems provide many economic, ecological, and environmental benefits for humans and their surroundings [
92]. Some of these services are unique, which rationalizes the importance of conserving mangrove ecosystems from degradation. One of the efficient approaches for mangrove mapping is to employ remote sensing data to frequently monitor these natural resources and take the necessary actions to avoid their further loss [
93]. In this regard, the machine learning algorithms, such as ANN, that were investigated in this paper can be incorporated to obtain highly accurate information about the mangrove ecosystems. Currently, satellite images with medium spatial resolutions, such as Sentinel-1, Sentinel-2, and Landsat archives, can be accessed with no cost, permitting monitoring of these ecosystems with optimized costs in spatial and temporal directions [
22]. Indeed, these satellites could be effectively employed by different national and local organizations regarding the preservation practices of mangrove ecosystems from decay. However, it should be considered that more accurate information, especially on sparse mangroves and narrow mangrove patches, requires the utility of satellite images with higher spatial resolutions, and the high cost of these images is the primary obligation [
94].
The ANN models proved to be capable of producing accurate mangrove ecosystem maps. Likewise, the results and further discussions could elucidate the path for other researchers to implement the ANN algorithm in other areas. Additionally, the proven potential of the ANN algorithm in this study may encourage other researchers to suppose achieving satisfactory results in other mangrove ecosystems even with more complex conditions. For instance, it is expected to obtain accurate results using the ANN algorithm in other mangrove ecosystems with other classes such as terrestrial forest, other wetland types, shrubs, and other vegetated communities. This is because other vegetated communities and wetlands have a higher rate of spectral similarity with mangroves [
95]. This would affect the classification procedure by decreasing the separability of classes and, thus, robust algorithms are vital for accurate mapping. Furthermore, Three ANN models performed satisfactorily, and the achieved PAs and UAs (see
Table 2) suggested that the ensemble of ANN models can enhance the results, which could be investigated in future studies. This is mainly due to the fact that each ANN model obtained higher accuracies in different classes over the study area.
In this study, the satellite images were collected from GEE. This cloud-based platform uses high-performance parallel computing that allows for the application of preprocessing steps on numerous images [
20,
68]. Consequently, it can help to reduce the required time for applying preprocessing steps that are almost repetitive procedures and can lead to a decrease in the dedicated time [
46,
96]. Despite these massive advantages, this cloud platform does not currently support ANN models in its base form (i.e., JavaScript API); hence, the experiments should be taken in another platform. Here, the Google Colab platform and the scikit-learn package were used in support of the implemented methodology so that it can be applied at a low cost by any users around the world.
The results of this study confirmed the capability of the implemented ANN model for mangrove ecosystem mapping. Accordingly, this algorithm could be implemented to produce an accurate baseline of mangrove ecosystems in any region. Additionally, the utility of charge-free satellite images (i.e., Sentinel-1 and Sentinel-2) and cloud computing platforms allow for frequent mangrove mapping at a low cost. In this regard, the temporal evolutions and changes in mangrove ecosystems can be mapped in previous years based on the availability of Sentinel-1 and Sentinel-2 images and the following years. This framework permits consistent monitoring of mangrove ecosystems and creates the opportunity to enact practical workflows to preserve these natural resources, especially in protected areas, from adverse anthropogenic and natural processes. Furthermore, frequent monitoring through such a reproducible approach can assist in assessing the applied practices, such as conservation planning and mangrove plantation, to support sustainable development.
5.2. Impact of Data Standardization
Input data transformation has been recognized as an impactful preprocessing task when using machine learning algorithms such as ANN models [
97]. Accordingly, the remote sensing data of this study were transformed using the standard scaler approach (i.e., removing the mean and scaling to unit variance). However, an analysis was also performed to investigate the impact of data transformation/standardization on ANN models based on three different learning algorithms. ANN models with different topologies were retrained using raw input features. Based on
Figure 9, all ANN models failed to retain their behavior in comparison to using transformed input data (see
Figure 4). In particular, the ANN models with the LBFGS and SGD learning algorithms failed to converge in almost all cases. However, the ANN model in which the Adam learning algorithm was employed performed more consistently. In fact, this analysis demonstrated the capability of the Adam learning algorithm to handle untransformed input data.
5.3. Impact of Limited Training Samples
It is already known that training samples are required to support the training phase of any supervised machine learning algorithm. Furthermore, it is accredited that collecting training samples, either through in situ field campaigns or visual interpretation of high-resolution images, is time consuming and resource intensive [
98]. Therefore, it is more convenient to develop efficient approaches or incorporate robust machine learning algorithms that require a limited number of training samples [
99,
100]. Accordingly, the impact of limited training samples on the performance of the ANN models was also explored. To this end, only a limited number of training samples from the original set (see
Section 3.1) was considered in this section. In particular, the number of training samples for each class was set between 10 and 500 to evaluate the performance of ANN models.
Figure 10 illustrates the outcome of using a limited number of training samples for mangrove ecosystem mapping. It is evident that three ANN models experienced an F-score fall compared with the case of using whole training samples. For example, the F-score values of the ANN models with the Adam, LBFGS, and SGD learning algorithms, respectively, decreased by 7%, 16%, and 7%, considering their best case using a limited number of training samples. Furthermore,
Figure 10 shows that the LBFGS relatively tends to obtain lower accuracies when the number of training samples was limited.
5.4. Impact of Noise Labels
The quality of training samples can directly influence the classification results. As such, the existence of noise labels (i.e., training samples with wrong labels), from any source, in the training samples can adversely affect the performance of machine learning algorithms [
101]. For instance, mislabeling of 25% of the training samples reduced the OA of the RF up to 10%. Additionally, based on experimental results, the KC obtained using the KNN classifier decreased by approximately 35% when 28% of the training samples had wrong labels [
101]. In this perspective, the performance of the best ANN models was examined in the case of the presence of noise labels. To this end, the labels of a portion (in percentage) of the training samples were randomly changed to other classes. It should be noted that an equal percentage was applied to training samples of each class. Additionally, the impact of noise labels was investigated based on a different number of training samples to identify whether there was any relationship between these two factors. It was observed (see
Figure 11) that ANN models were minorly affected by noise labels when the noise label percentage was set between 1% and 100%, and a more dramatic decrease in the F-score occurred when over 60% of training samples were subjected to label change. Furthermore, no direct relationship was found between the number of training samples and noise labels.
5.5. Contribution of Multi-Temporal and Multi-Source Images
Using multi-temporal and multi-source remote sensing imagery is recognized as a practical approach to obtaining higher land cover (e.g., wetlands) classification results [
102]. This is rooted in the fact that different remote sensing data sources could provide complementary information of the Earth’s surface. For instance, multi-spectral and SAR data sets provide spectral and physical properties of the Earth’s surface, respectively. Furthermore, multi-temporal remote sensing could provide discriminative information about different classes with dynamic characteristics, reducing the confusion of existing classes and improving the classification results [
21,
103].
Regarding the contribution of the multi-source remote sensing data, the best ANN models were incorporated to map the mangrove ecosystem using single-source data sets. It was observed that the obtained F-score value using multi-spectral (i.e., Sentinel-2) images for the best ANN model was 0.95, which was nearly 2% lower than using multi-source data sets. Furthermore, the results revealed that incorporating only the SAR data set could not achieve satisfactory results (i.e., F-score = 0.75). The results indicated that the utility of multi-source remote sensing could enhance the classification results and could lead to accurate mangrove maps. This improvement would be more considerable in locations with more complex conditions [
65].
Moreover, the best ANN model was employed to examine the effects of using multi-temporal data sets in the classification results. In this regard, single-season data sets were fed into the best ANN model, and it was observed that no single-season data set achieved higher classification accuracy. In particular, the spring, summer, autumn, and winter data sets achieved F-score values of 0.94, 0.91, 0.92, and 0.92, respectively. These values were 3%, 6%, 5%, and 5% lower than the accuracy of the map using multi-temporal data sets.