Abstract
Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps. This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran. To this end, multi-temporal synthetic aperture radar (SAR) and multi-spectral remote sensing data from Sentinel-1 and Sentinel-2 were processed in the Google Earth Engine (GEE) cloud computing platform. Afterward, the ANN topologies and specifications considering the number of layers and neurons, learning algorithm, type of activation function, and learning rate were examined for mangrove ecosystem mapping. The results indicated that an ANN model with four hidden layers, 36 neurons in each layer, adaptive moment estimation (Adam) learning algorithm, rectified linear unit (Relu) activation function, and the learning rate of 0.001 produced the most accurate mangrove ecosystem map (F-score = 0.97). Further analysis revealed that although ANN models were subjected to accuracy decline when a limited number of training samples were used, they still resulted in satisfactory results. Additionally, it was observed that ANN models had a high resistance when training samples included wrong labels, and only the ANN model with the Adam learning algorithm produced an accurate mangrove ecosystem map when no data standardization was performed. Moreover, further investigations showed the higher potential of multi-temporal and multi-source remote sensing data compared to single-source and mono-temporal (e.g., single season) for accurate mangrove ecosystem mapping. Overall, the high potential of the proposed method, along with utilizing open-access satellite images and big-geo data processing platforms (i.e., GEE, Google Colab, and scikit-learn), made the proposed approach efficient and applicable over other study areas for all interested users.
Highlights
Mangrove ecosystems are among the most productive ecosystems that exist along coastal areas in tropical and sub-tropical regions
The results demonstrate the effect of different learning algorithms for mangrove ecosystem mapping using the artificial neural networks (ANNs) algorithm
The results indicated the high potential of the ANN models, especially the ANN model with the adaptive moment estimation (Adam) learning algorithm for mangrove ecosystem mapping (F-score = 0.97)
Summary
Mangrove ecosystems are among the most productive ecosystems that exist along coastal areas in tropical and sub-tropical regions. 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 [11–14]. 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. 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]
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