Artificial Intelligence-Enabled Mangrove Ecosystem Monitoring Using Remote Sensing and Environmental Data

Dirvi Surya Abbas; Asep  Sutarman; Ryan Davis; Maulana  Abbas

doi:10.68012/air.v1i2.252

Authors

Dirvi Surya Abbas University of Muhammadiyah Tangerang Author https://orcid.org/0000-0002-7819-2837
Asep Sutarman University of Muhammadiyah Prof. Dr. HAMKA Author https://orcid.org/0009-0002-1029-7963
Ryan Davis Pandawan Incorporation Author
Maulana Abbas Universitas Raharja Author https://orcid.org/0009-0009-0137-9650

DOI:

https://doi.org/10.68012/air.v1i2.252

Keywords:

Artificial Intelligence, Mangrove Ecosystem Monitoring, Remote Sensing, Convolutional Neural Network, Environmental Resilience

Abstract

Mangrove ecosystems play a critical role in coastal protection, carbon sequestration, biodiversity conservation, and climate change mitigation; however, increasing anthropogenic pressures and environmental changes have accelerated mangrove degradation, creating an urgent need for efficient and scalable monitoring approaches. This study aims to develop an Artificial Intelligence-Enabled framework for monitoring mangrove ecosystem conditions by integrating remote sensing imagery with environmental datasets to improve the accuracy and timeliness of ecosystem assessment in tropical coastal regions. The proposed method combines multispectral satellite images, including vegetation indices derived from remote sensing data, with environmental variables such as temperature, precipitation, salinity, and tidal information, which are subsequently processed using a deep learning-based classification model to identify and categorize mangrove health conditions. Experimental evaluation demonstrates that the integration of remote sensing and environmental data significantly enhances model performance compared with approaches relying solely on satellite imagery, achieving high classification accuracy and improving the detection of early signs of ecosystem degradation. The findings further reveal that environmental parameters contribute substantially to distinguishing healthy, moderately degraded, and severely degraded mangrove areas across heterogeneous coastal environments. Consequently, the proposed framework provides an intelligent and reliable decision support tool for environmental monitoring agencies and policymakers while contributing to the development of resilient coastal ecosystem management and sustainable environmental governance in tropical archipelagic regions.

References

[1] R. Sunkur, K. Kantamaneni, C. Bokhoree, U. Rathnayake, and M. Fernando, “Mangrove mapping and monitoring using remote sensing techniques towards climate change resilience,” Scientific Reports, vol. 14, no. 1, p. 6949, 2024.

[2] Z. Sun, W. Jiang, Z. Ling, S. Zhong, Z. Zhang, J. Song, and Z. Xiao, “Using multisource high-resolution remote sensing data (2 m) with a habitat–tide–semantic segmentation approach for mangrove mapping,” Remote Sensing, vol. 15, no. 22, p. 5271, 2023.

[3] Y. N. Qintharah and F. L. Utami, “Determinants on environmental disclosure moderating by integrated corporate governance,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 1Sp, pp. 22–37, 2023.

[4] Y. Wei, Y. Cheng, X. Yin, Q. Xu, J. Ke, and X. Li, “Deep learning-based classification of high-resolution satellite images for mangrove mapping,” Applied Sciences, vol. 13, no. 14, p. 8526, 2023.

[5] M. Dimyati, D. A. Umarhadi, I. Jamaluddin, D. Awanda, and W. Widyatmanti, “Mangrove monitoring revealed by mdprepost-net using archived landsat imageries,” Remote Sensing Applications: Society and Environment, vol. 32, p. 101041, 2023.

[6] H. Dong, Y. Gao, R. Chen, and L. Wei, “Mangroveseg: Deep-supervision-guided feature aggregation network for mangrove detection and segmentation in satellite images,” Forests, vol. 15, no. 1, p. 127, 2024.

[7] A. Dudhat and E. Dolan, “Evaluation of ecopreneurship business sustainability in the context of the digital economy,” Startupreneur Business Digital (SABDA Journal), vol. 4, no. 1, pp. 64–72, 2025.

[8] Z. Chen, M. Zhang, H. Zhang, and Y. Liu, “Mapping mangrove using a red-edge mangrove index (remi) based on sentinel-2 multispectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–11, 2023.

[9] L. Tan and H. Wu, “Artificial intelligence mangrove monitoring system based on deep learning and sentinel-2 satellite data in the uae (2017-2024),” arXiv preprint arXiv:2411.11918, 2024.

[10] J. Vasquez, R. Acevedo-Barrios, W. Miranda-Castro, M. Guerrero, and L. Meneses-Ospina, “Determining changes in mangrove cover using remote sensing with landsat images: A review,” Water, Air, & Soil Pollution, vol. 235, no. 1, p. 18, 2024.

[11] E. R. Lines, M. Allen, C. Cabo, K. Calders, A. Debus, S. W. Grieve, M. Miltiadou, A. Noach, H. J. Owen, and S. Puliti, “Ai applications in forest monitoring need remote sensing benchmark datasets,” in 2022 IEEE international conference on big data (big data). IEEE, 2022, pp. 4528–4533.

[12] R. Evans, F. P. Oganda, M. A. Setiawan, L. Nurjanah, and M. Sunengsih, “Assessing the environmental and economic effects of smart grid integration using sem,” International Transactions on Artificial Intelligence, vol. 4, no. 1, pp. 49–60, 2025.

[13] S. Wu, Y. Zhao, Y. Wang, J. Chen, T. Zang, and H. Chen, “Convolution feature inference-based semantic understanding method for remote sensing images of mangrove forests,” Electronics, vol. 12, no. 4, p. 881, 2023.

[14] G. Lassalle, M. P. Ferreira, L. E. C. La Rosa, R. D. M. Scafutto, and C. R. de Souza Filho, “Advances in multi-and hyperspectral remote sensing of mangrove species: A synthesis and study case on airborne and multisource spaceborne imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 195, pp. 298–312, 2023.

[15] J. Heo, B. Choi, H. Shin, and D. Kwon, “Mango: A global single-date paired dataset for mangrove segmentation,” arXiv preprint arXiv:2601.17039, 2026.

[16] J. Peng and J. Zhang, “Spatiotemporal assessment of urban flooding hazard using social media: A case study of zhengzhou ‘7· 20’,” Environmental Modelling & Software, vol. 176, p. 106021, 2024.

[17] S. Purnama, B. L. Pradana, G. Khanna, S. Suhandi, A. Rizky, I. N. Hikam, and M. F. Kamil, “The impact of war on the cryptocurrency economy from a management perspective,” International Journal of Cyber and IT Service Management (IJCITSM), vol. 4, no. 2, pp. 143–154, 2024.

[18] S. L. Lestari, H. Mayaguezz, and I. G. Febryano, “Artificial intelligence in mangrove management policy in indonesia: A systematic literature review,” Jurnal Perikanan, vol. 15, no. 6, Dec. 2025. [Online]. Available: https://garuda.kemdiktisaintek.go.id/documents/detail/5871578

[19] A. Ghorbanian, S. A. Ahmadi, M. Amani, A. Mohammadzadeh, and S. Jamali, “Application of artificial neural networks for mangrove mapping using multi-temporal and multi-source remote sensing imagery,” Water, vol. 14, no. 2, p. 244, 2022.

[20] A. A. Al-Huqail, Z. Islam, H. F. Al-Harbi, and F. Khan, “Ai-driven mangrove mapping on farasan islands, saudi arabia: enhancing the detection of dispersed patches with ml classifiers,” Scientific Reports, vol. 15, no. 1, p. 19285, 2025.

[21] P. Ilamathi and S. Chidambaram, “Integration of hyperspectral imaging and deep learning for sustainable mangrove management and sustainable development goals assessment,” Wetlands, vol. 45, no. 1, p. 9, 2025.

[22] Z. Xue, S. Qian, G. Camps-Valls, M. Jia, H. Zhang, and K. Khoshelham, “Ai in mangrove remote sensing: A comprehensive review,” IEEE Geoscience and Remote Sensing Magazine, 2026.

[23] O. F. Ngabito, D. Huldiansyah, A. N. Sofyan, M. B. Kurniawan, S. A. Pratama et al., “Design and development of an augmented reality learning medium for the conservation of the endemic flora balikpapan ginger (etlingera balikpapanensis),” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 8,no. 1, pp. 1–11, 2026

[24] S. M. R. Alam and M. S. Hossain, “Artificial intelligence and remote sensing for coastal ecosystem service assessment and climate-resilient management: Ai-eyed guardians of resilient coasts,” in AI and Remote Sensing for Earth Sciences. IGI Global Scientific Publishing, 2026, pp. 157–202.

[25] M. R. Kumar and K. Neppolian, “Harnessing artificial intelligence and remote sensing for large-scale marine habitat monitoring and conservation,” Journal of Animal Environment, vol. 17, no. 3, pp. 391–402, 2025.

[26] G. Ali, M. M. Mijwil, I. Adamopoulos, and J. Ayad, “Leveraging the internet of things, remote sensing, and artificial intelligence for sustainable forest management,” Babylonian Journal of Internet of Things, vol. 2025, pp. 1–65, 2025.

[27] A. Sartono, M. Darmawan, F. Ramadhani, M. Ramdhan, S. Safitri, and B. Sutejo, “Environmental monitoring of shallow marine waters using ai and remote sensing: A systematic literature review,” Environmental Quality Management, vol. 35, no. 1, p. e70148, 2025.

[28] K. K. Ramteke, S. Akter, P. Vineeth, S. Ronad, M. J. Barman, and S. Thanga Anusya, “Integrating remote sensing and geographic information science for sustainable coastal and marine ecosystem management,” in Monitoring Coastal Marine Ecosystems Under Climate Change: Machine Learning and Artificial Intelligence Techniques. Springer, 2026, pp. 381–409.

[29] R. Veeranjaneyulu, D. Govindarajan, C. Subramanian, D. U. Devi, S. Banerjee, S. K. Edpuganti, and S. Upadhyay, “Marine ecosystem monitoring: applying remote sensing and ai to track and predict coral reef health,” Remote Sensing in Earth Systems Sciences, vol. 7, no. 4, pp. 486–499, 2024.

[30] E. P. Harahap, E. Sediyono, Z. A. Hasibuan, U. Rahardja, and M. T. Dayanti, “Thematic analysis smart tourism ecosystems: A decade of technological advancement and sustainable impact,” Human Behavior and Emerging Technologies, vol. 2026, no. 1, p. 8688311, 2026.

[31] A. Amroni, A. A. Darmawan, and G. A. Pangilinan, “The role of big data and blockchain in enabling transparent and sustainable business processes,” ADI Journal on Recent Innovation (AJRI), vol. 6, no. 2, pp. 180–189, 2025.

[32] A. N. T. Do, T. A. T. Do, L. Van Pham, and H. D. Tran, “Assessment of the role of mangroves for periophthalmus modestus applying machine learning and remote sensing: a case study in a large estuary from vietnam,” Aquatic Ecology, vol. 58, no. 3, pp. 879–893, 2024.

[33] D. Sch¨urholz, G. A. Castellanos-Galindo, E. Casella, J. C. Mej´ıa-Renter´ıa, and A. Chennu, “Seeing the forest for the trees: Mapping cover and counting trees from aerial images of a mangrove forest using artificial intelligence,” Remote Sensing, vol. 15, no. 13, p. 3334, 2023.

[34] D. M. Kristari and A. Y. Teruna, “Impact of carbon emission disclosure and corporate social responsibility on indonesian manufacturing companies,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 1Sp, pp. 38–48, 2023.

[35] B. Gizachew, “Artificial intelligence and machine learning in remote sensing for tropical forest monitoring: Applications, challenges, and emerging solutions,” Remote Sensing, vol. 18, no. 8, p. 1193, 2026.