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A Synergic Use of Sentinel-1 and Sentinel-2 Imagery for Complex Wetland Classification Using Generative Adversarial Network (GAN) Scheme
Article

Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery

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Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
2
Wood Environment & Infrastructure Solutions, Ottawa, ON K2E 7L5, Canada
3
Department of Technology and Society, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: Chang Huang
Water 2022, 14(2), 244; https://doi.org/10.3390/w14020244
Received: 20 November 2021 / Revised: 22 December 2021 / Accepted: 12 January 2022 / Published: 15 January 2022
(This article belongs to the Special Issue Mapping and Monitoring of Wetlands)
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. View Full-Text
Keywords: mangrove; artificial neural networks (ANNs); Sentinel-1; Sentinel-2; Google Earth Engine (GEE); multi-temporal; multi-source; remote sensing mangrove; artificial neural networks (ANNs); Sentinel-1; Sentinel-2; Google Earth Engine (GEE); multi-temporal; multi-source; remote sensing
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MDPI and ACS Style

Ghorbanian, A.; Ahmadi, S.A.; Amani, M.; Mohammadzadeh, A.; Jamali, S. Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery. Water 2022, 14, 244. https://doi.org/10.3390/w14020244

AMA Style

Ghorbanian A, Ahmadi SA, Amani M, Mohammadzadeh A, Jamali S. Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery. Water. 2022; 14(2):244. https://doi.org/10.3390/w14020244

Chicago/Turabian Style

Ghorbanian, Arsalan, Seyed A. Ahmadi, Meisam Amani, Ali Mohammadzadeh, and Sadegh Jamali. 2022. "Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery" Water 14, no. 2: 244. https://doi.org/10.3390/w14020244

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