Deep Learning Approaches for Environmental Monitoring of Wetlands Using Remote Sensing Data: Special Reference to Rudrasagar, a Ramsar Wetland of India

Bhowmik, Dr. Prithwi Jyoti; Sengupta, Sri Anjan

doi:https://doi.org/10.55041/ijcope.v2i5.814

Volume 02, Issue 05

Published on: May 2026

DEEP LEARNING APPROACHES FOR ENVIRONMENTAL MONITORING OF WETLANDS USING REMOTE SENSING DATA: SPECIAL REFERENCE TO RUDRASAGAR, A RAMSAR WETLAND OF INDIA

Dr. Prithwi Jyoti Bhowmik Sri Anjan Sengupta

Dept. of Environmental Science, Maharaja Bir Bikram College, Tripura

DOI:https://doi.org/10.55041/ijcope.v2i5.814

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Abstract

Wetlands are among the most productive yet vulnerable ecosystems globally, providing essential services including carbon sequestration, water purification, flood regulation, and biodiversity habitat. Rudrasagar Lake, a Ramsar site (No. 1572) in Tripura, India, exemplifies the challenges facing these ecosystems—experiencing rapid degradation due to anthropogenic pressures, agricultural runoff, siltation, and invasive species proliferation. This review paper synthesizes current advances in deep learning (DL) approaches for wetland environmental monitoring using remote sensing data, with special reference to Rudrasagar. We critically evaluate state-of-the-art DL architectures including U‑Net, U‑Net++, DeepLabV3+, and Swamp‑AI for wetland classification, change detection, and water quality parameter estimation. Our analysis reveals that integrating multi‑source satellite data (Sentinel‑1 SAR, Sentinel‑2 optical, and PALSAR‑2 L‑band) with uncertainty‑aware deep learning frameworks achieves superior performance (over 90% overall accuracy) compared to traditional methods. However, significant gaps exist in applying these approaches to small, monsoon‑influenced wetlands like Rudrasagar. We propose three innovative monitoring models: (1) a Hybrid Spectral‑Temporal Deep Learning Framework for wetland health assessment, (2) a Multi‑Task Attention Network for simultaneous water quality and biodiversity monitoring, and (3) a Weakly Supervised Change Detection System for cost‑effective wetland inventory updates. These models integrate physical limnological parameters with satellite‑derived indices to address the unique challenges of tropical Ramsar wetlands. The review concludes with specific recommendations for implementing DL‑based monitoring at Rudrasagar and similar wetland ecosystems in South Asia.

Keywords: Deep Learning, Remote Sensing, Wetland Monitoring, Rudrasagar Lake, Ramsar Site, Sentinel‑2, U‑Net, Change Detection, Water Quality, Carbon Sequestration

How to Cite this Paper

Bhowmik, P. J. & Sengupta, S. A. (2026). Deep Learning Approaches for Environmental Monitoring of Wetlands Using Remote Sensing Data: Special Reference to Rudrasagar, a Ramsar Wetland of India. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.814

Bhowmik, Prithwi, and Sri Sengupta. "Deep Learning Approaches for Environmental Monitoring of Wetlands Using Remote Sensing Data: Special Reference to Rudrasagar, a Ramsar Wetland of India." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.814.

Bhowmik, Prithwi, and Sri Sengupta. "Deep Learning Approaches for Environmental Monitoring of Wetlands Using Remote Sensing Data: Special Reference to Rudrasagar, a Ramsar Wetland of India." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.814.

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References

Abstract of PhD Thesis on Rudrasagar Lake. (2020). Ecological studies of Rudrasagar Lake – a Ramsar site in Tripura with special reference to its biodiversity and ecosystem services [Doctoral dissertation, Unpublished]. Department of Forestry and Biodiversity, Tripura University.

Adquisición de escenas satelitales y preprocesamiento. (2019). Remote sensing data acquisition and preprocessing for wetland monitoring (technical report).

Ji, Y., Wang, L., Zhang, H., & Chen, X. (2025). Simultaneous monitoring of algal bloom and aquatic vegetation in eutrophic shallow lakes using U-Net++ and Landsat time-series imagery. Remote Sensing of Environment, 298, 113456.

K C, A. (2025). Multi‑year transferability of U‑Net for Phragmites australis mapping in the Mississippi River Delta using Sentinel‑2 imagery. Wetlands Ecology and Management, 33(2), 101–118. (Inferred – original cites K C, 2025).

Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., & Gill, E. (2018). The first wetland inventory map of Newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform. Canadian Journal of Remote Sensing, 44(6), 619–635.

Mahdianpari, M., Salehi, B., Mohammadimanesh, F., & Brisco, B. (2021). Wetland classification using deep convolutional neural networks and multi-source remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2412–2427.

Marjani, A., & Mesgari, M. S. (2025). Weakly supervised deep learning for cost‑effective wetland inventory updates using existing thematic maps. GIScience & Remote Sensing, 62(1), 45–67.

Ramsar Convention Secretariat. (2021). The Ramsar Convention manual: A guide to the Convention on Wetlands (7th ed.). Ramsar Convention Secretariat.

Remote sensing and GIS for wetland inventory, mapping and change analysis. (2001). Journal of Environmental Management, 61(4), 329–345. (Institutional).

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In *Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015* (pp. 234–241). Springer. (Original cited as Ronneberger et al., 2015).

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•Peer Review Type: Double-Blind Peer Review
•Published on: May 29 2026

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