Seasonal Dynamics of Coastal Landscapes: A Critical Review Using Remote Sensing and GIS

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 17 | 02 | Page :
    By

    K. Nagalakshmi,

  • C. Lakshminarayana,

  • J. Sreeramulu,

  • P. Jayalakshmi,

  • P. Padma Sree,

  1. Lecturer, Department of Geology, Government College (Autonomous), Anantapur, Andhra Pradesh, India
  2. Lecturer, Department of Geology, Government College (Autonomous), Anantapur, Andhra Pradesh, India
  3. Lecturer, Department of Geology, Government College (Autonomous), Anantapur, Andhra Pradesh, India
  4. Lecturer, Department of Geology, Government College (Autonomous), Anantapur, Andhra Pradesh, India
  5. Lecturer, Department of Geology, Government College (Autonomous), Anantapur, Andhra Pradesh, India

Abstract

Coastal landscapes are among the most dynamic environments on Earth, undergoing continuous transformation due to both natural processes and anthropogenic activities. In India, particularly along the southern coastal regions of Andhra Pradesh, Tamil Nadu, and Kerala, shoreline morphology and sediment transport patterns are significantly influenced by seasonal monsoons, cyclones, storm surges, waves, tides, and changing river discharges. These factors contribute to varying rates of coastal erosion, accretion, inundation, and land- use changes, posing challenges for sustainable coastal management. This review critically examines the application of geospatial technologies, particularly Remote Sensing (RS) and Geographic Information Systems (GIS), in monitoring and assessing seasonal coastal changes. Various methodologies, including supervised and unsupervised Land Use/Land Cover (LULC) classification, spectral indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified NDWI (MNDWI), as well as multi-temporal shoreline extraction and change detection techniques, are comprehensively evaluated. Selected case studies from southern India demonstrate the effectiveness of these approaches in capturing seasonal variability, shoreline migration, and geomorphological transformations. The review further discusses key challenges, including cloud contamination, spatial and temporal resolution constraints, data availability, and methodological uncertainties. The emerging advancements such as machine learning algorithms, artificial intelligence-based classification, and Synthetic Aperture Radar (SAR) integration are highlighted for their potential to enhance the accuracy, reliability, and continuity of coastal monitoring and decision-making processes.

Keywords: Coastal Dynamics; Seasonal Variability; Remote Sensing; GIS; Shoreline Change; LULC; Spectral Indices; Southern India.

How to cite this article:
K. Nagalakshmi, C. Lakshminarayana, J. Sreeramulu, P. Jayalakshmi, P. Padma Sree. Seasonal Dynamics of Coastal Landscapes: A Critical Review Using Remote Sensing and GIS. Journal of Remote Sensing & GIS. 2026; 17(02):-.
How to cite this URL:
K. Nagalakshmi, C. Lakshminarayana, J. Sreeramulu, P. Jayalakshmi, P. Padma Sree. Seasonal Dynamics of Coastal Landscapes: A Critical Review Using Remote Sensing and GIS. Journal of Remote Sensing & GIS. 2026; 17(02):-. Available from: https://journals.stmjournals.com/jorsg/article=2026/view=246854


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Ahead of Print Subscription Review Article
Volume 17
02
Received 09/05/2026
Accepted 03/06/2026
Published 17/06/2026
Publication Time 39 Days
12

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