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LIRR: A MULTIMODEL MACHINE LEARNING APPROACH TO DETECT HIGH-RESOLUTION LANDSLIDE INDUCED ROAD-RISK AND ASSOCIATED RISK BUDGETING FOR UTTRAKHAND, INDIA
LIRR: A MULTIMODEL MACHINE LEARNING APPROACH TO DETECT HIGH-RESOLUTION LANDSLIDE INDUCED ROAD-RISK AND ASSOCIATED RISK BUDGETING FOR UTTRAKHAND, INDIA
IEEE International Geoscience and Remote Sensing Symposium - 2025 • Brisbane, Australia
Subhajit Bandopadhyay Sourav Dey . . . . .
Leveraging Mission TRISHNA for Synergistic Monitoring of Crop Health and Early Disease Detection in Support of Global Food Security.
IEEE International Geoscience and Remote Sensing Symposium - 2025 • Brisbane, Australia
Subhajit Bandopadhyay Sourav Dey . . . . .
Recently, near-infrared reflectance of vegetation (NIRv ) provides a promising way to quantify crop-water stress and early disease detection due to its direct relation with the core of plant photosynthetic aperture. NIRv defined as the product of observed NIR reflectance and normalized difference vegetation index (NDVI) can accurately capture the changes in both plant productivity gradient and chlorophyll content. This dual sensitivity makes NIRv a valuable tool for monitoring crop health in conjunction with Sun-induced fluorescence (SIF) evidenced by several studies. In order to justify the primary objective of the upcoming TRISHNA mission, in this study we are proposing to quantify crop water stress and early disease detection through the synergy between NIRv , crop water stress index (CWSI), and Leaf area index (LAI) that provides vital information about plant responses under various circumstances. The availability of seven surface reflectance bands in the visible to shortwave infrared (VSWIR) spectrum, particularly the red and NIR bands, facilitates the development of the NIR v and LAI. Additionally, two thermal infrared (TIR) payloads will be instrumental in estimating the crop water stress index (CWSI). Moreover, the proposed indexes and their interrelationship can be applied from sub-regional to regional scale, varying from crop types and local climatic conditions. The study hypothesized that integration of NIRv , LAI, and CWSI will provide a more accurate assessment of crop water stress and early disease detection compared to using any single index alone, as these metrics collectively capture physiological responses of plants under varying environmental conditions even under different crop types. The authors believe the outputs generated from the proposed study by leveraging the TRISHNA mission will be supported as Essential Agricultural Variables (EAVs), and Essential Climate Variables (ECVs) for sustainable agricultural practices and informing policy frameworks aimed at addressing global food security and climate change challenges.
From Satellite to Safety: Leveraging Earth Observation For Advancing Microscale Hazard Management Strategies.
From Satellite to Safety: Leveraging Earth Observation For Advancing Microscale Hazard Management Strategies.
Natural Hazards Engineering Research Infrastructure (NHERI) Graduate Student Council (GSC) Mini Conference
Sourav Dey Subhajit Bandopadhyay
This study undertakes a comprehensive analysis of surface temperature, drought severity, and flood vulnerability in a densely populated area of Ahmedabad over 25 years (2000-2024) using the fusion of SAR and multispectral data. The study region is prone to a range of microclimate-induced hazards, including extreme heat, drought, and floods. Our findings reveal a complex interplay between hazards, urbanization, and anthropogenic activities over some time. The study shows that the yearly mean land surface temperature (LST) has decreased by 4°C from 2000 to the present (2024), due to increased green spaces and sustainable urban planning measures. However, this trend is not uniform across the city, with areas exhibiting higher LST values indicating poor unplanned urbanization and lack of green spaces. The region is also characterized as highly drought-prone, represented by the Palmer Drought Severity Index (PDSI) which consistently indicates severe drought conditions. Although the region is prone to drought, floods are another significant threat to Ahmedabad City, being a coastal land, experiencing heavy rainfall and water logging during monsoon. Observation reveals a probable threat of waterlogging, despite the absence of flooded pockets last year. The statistics underscore the extensive impact on infrastructure and livelihoods. This study on hazard dynamics offers valuable insights for managing urban micro-scale hazards, boosting urban resilience, and providing crucial guidance for policymakers, urban planners, and stakeholders in Ahmedabad City and comparable regions globally.
Sensing The Dynamics of Small Landholding in India through Earth Observation: A Comprehensive Review
Subhajit Bandopadhyay Sourav Dey Latika Grover Subhasis Ghosh Barnali Das
DOI: http://dx.doi.org/10.31223/X5N13S
[ Preprint ]
Fragmentation or the breakdown of landholdings to smaller parcels has an adverse impact on crop yields and productivity because of its uneconomic operational sizes. This comprehensive review reflects the insights into the complex dynamics of small landholding (SLs) in India by leveraging earth observation (EO) based sensing technology through synthesizing existing literature, methodologies and outcomes, and technological advancements along with its challenges and limitations. This study aims to summarize the current state-of-the problems, management, and utilization of EO technology for mapping, monitoring, and parametric assessment of small-scale agricultural land holdings in India. The review also discussed about different sensing platforms, and how to utilize their varied spectrums for identifying and characterizing SLs at different geographies of India. India, with its deeply rooted agricultural tradition dating back thousands of years, has agriculture as the primary occupation for a significant portion of its 1.4 billion population. However, a significant issue in Indian agriculture is the fragmentation of croplands into small landholdings, which results in the division of agricultural land into smaller and often uneconomical parcels. Therefore, accurate delineation and EO based sensing of SLs in India is highly necessary for precise monitoring of crop health, soil conditions, and water usage and many more, which can significantly improve productivity on small, uneconomical parcels of land to boost nations food security. The Authors believe with the efficient intervention of EO based sensing technology in SLs, farmers can make more informed decisions and maximize their yields by making more effective use of their resources. Furthermore, Earth observation can help prevent crop losses and improve food security in a nation where a large portion of the population is dependent on agriculture by helping with the early detection of pests and diseases. Our study will support the decision-making process and policy formulation in Indian agriculture system by providing comprehensive insights from EO-based sensing perspectives. Finally, this will help to create more productive and sustainable farming methods, which will be advantageous to both farmers and the national economy.
Research Interest
Geospatial Modeling in Precision Agriculture.
• EO based-ML Prediction Model for Hazard Vulnerability and Risk Assessment.
• Climate Change Impact Analysis over Global and Regional Environments.
• Remote Sensing and GIS Modeling Application in Cryosphere.
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