Browsing by Author "Pazhanivelan, S"

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    Assessment of Climate Change on Soil Erosion Using Geospatial Techniques: A Review
    (Ms. M. B. Mondal, Ph.D., 2025-06) Pazhanivelan, S; Lad, SU; Selvakumar, S; Ravikumar, V; Ramesh, AV; Karale, OS; Jadhav, RJ.
    Climate change is accelerating soil erosion, presenting a significant threat to food security and ecosystem health globally. This review investigates the impact of climate change on soil erosion using advanced geospatial methods and the Revised Universal Soil Loss Equation (RUSLE). An analysis of over 100 recent peer-reviewed articles (including research from 1990 to 2024) explores how factors such as shifting precipitation patterns, rising temperatures, extreme weather events, and land-use changes influence erosion across various scales. Climate change-induced shifts in precipitation patterns and intensifying weather events significantly affect soil erosion rates. Heavy rainfall events can cause substantial soil displacement, while droughts dry out the soil, leaving it vulnerable to wind erosion. Rising temperatures further exacerbate the problem by altering soil moisture levels and influencing vegetation cover, a crucial factor in erosion control. Land-use changes, including deforestation, urbanization, and agricultural practices, disturb soil stability and increase erosion rates. Remote sensing, GIS, and AI-machine learning are increasingly combined with advanced RUSLE variations to identify erosion hotspots with greater precision. These technologies enable monitoring of spatiotemporal patterns and assessment of future risks under various climate scenarios. Remote sensing techniques allow for high-resolution mapping of erosion-prone areas, while AI and machine learning enhance predictive models, providing more targeted and effective adaptation strategies. Despite potential temporary reductions in erosion in regions experiencing initial increases in vegetation cover, projections indicate a significant global soil loss increase by mid-century (2050). This increase is driven by heavier precipitation, intensifying droughts, and more frequent and severe floods. The resulting erosion can have devastating effects on agricultural productivity, water quality, and biodiversity. Integrated soil conservation practices, such as reduced tillage, cover cropping, and revegetation, are essential for building resilience against this growing threat. These practices help stabilize soil, improve water retention, and enhance the land's overall health. In addition, landscape management techniques, including contour farming and agroforestry, can further mitigate erosion risks. Advancements in AI-machine learning-based erosion prediction models offer promising opportunities for more precise and timely interventions. By integrating these models with remote sensing data, researchers can develop more accurate risk assessments and design more efficient mitigation strategies. High-resolution remote sensing allows for continuous monitoring and evaluation of erosion patterns, enabling adaptive management approaches. As results emerging technologies and innovative management practices offer new tools and approaches to address this challenge. By investing in research and adopting advanced geospatial methods, policymakers and stakeholders can work together to develop more effective strategies for mitigating soil erosion and safeguarding global food security and biological systems.
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    Enhancing Flood Area Mapping Accuracy Using Advanced SAR Data Processing
    (Ms. M. B. Mondal, 2024-12) Pazhanivelan, S; Ragunath, KP; Sudarmanian, N; Satheesh, S; Sneka, K.
    Aim: To assess the spatial distribution of floods in 2024 using remote sensing data, specifically Synthetic Aperture Radar (SAR), a powerful tool in flood monitoring and mapping due to its ability to capture data under all weather conditions, including rain and cloud cover provides high-resolution imagery suitable for identifying and analyzing flood extents.  Study Area and Duration: North-Eastern districts of Tamil Nadu viz., Tiruvannamalai, Ranipet, Chengalpat, Kancheepuram, Tiruvallur, Viluppuram, Cuddalore, Nagapattinam, Thanjavur, Tiruvarur, Kallakurichi and Mayiladuthurai. Methodology: Flood mapping uses imagery collected from the European Space Agency's (ESA) Sentinel-1A satellite to identify and map flooded regions. Flood mapping receives assistance from this satellite's C-band SAR sensor, which can capture images in any weather condition without affecting the data. Results: The flood vulnerability assessment using Sentinel-1A satellite data has provided critical insights into the extent and impact of flooding across Tamil Nadu in 2024. With a total of 90,369 hectares of agricultural land affected, the study highlights the urgency of implementing targeted flood management strategies. 350 ground truth points were collected, out of which 309 points coincided with the flood-affected areas. Among these 309 points, 214 were flood points and 95 were non-flood points. The overall accuracy of the results was 90.00 per cent. The producer and user accuracy for flood-affected areas was 92.10 per cent and 93.40 per cent, respectively. The producer and user accuracy for non-flood areas. was 85.30 per cent and 82.70 per cent with Kappa index of 0.80. Conclusion: These findings underscore the importance of integrating advanced remote sensing technologies with ground-level data to better understand flood dynamics and provides a foundation for sustainable disaster risk management and resource allocation, ensuring long-term agricultural and environmental security in Tamil Nadu.
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    Using Sentinel 1A (SAR) and Sentinel 2 Data for Assessing Water Spread Dynamics and Crop Diversification in Lower Palar Sub Basin, Tamil Nadu, India
    (Ms. M. B. Mondal, Ph.D., 2025-02) Pazhanivelan, S; Vairavamani, M; Ragunath, K; Kumaraperumal, R; Sudarmanian, NS; Manikandan, S; Satheesh, S.
    Sentinel-1A Synthetic Aperture Radar (SAR) satellite is highly beneficial for continuously monitoring the evaluating changes in agricultural areas and water spread area assessment. Having reliable information about water availability is crucial for effective regional planning. By analyzing water spread dynamics using SAR satellite data at the tank level, farmers can access more accurate and timely information, aiding in crop planning locally and regionally and improving water management practices. Utilizing SAR satellite data to track water spread is essential for addressing these challenges and enhancing agricultural productivity. This approach allows stakeholders to make better decisions about water resource allocation, promoting sustainable agriculture and water conservation. This study focused on the water spread area in Lower Palar tanks by analyzing multi-temporal Sentinel-1A SAR data, linking it to rainfall and cropping pattern changes in and around the command areas. The years 2020-2023 showed increased water spread compared to 2018-2019, suggesting improved rainfall distribution and potential for year-round cropping using Northeast monsoon rainfall for subsequent seasons. The study applied Random Forest machine learning for crop classification across seasons using Sentinel-2 optical datasets, leveraging the algorithm's accuracy and efficient handling of large datasets to understand how water availability affects crop diversification in the Lower Palar Sub-Basin. The crop diversification confirmed through diversity index. The SID value of 0.59 was obtained in the Summer 2018, due to the even distribution of (n) number of crops like paddy, groundnut, sugarcane and watermelon. The lowest SID value (0.21) was observed in Rabi 2021 due to higher water spread and the adoption of mono cropping in larger areas.