Mississippi State University
Geosystems Research Institute
About GRI
Established in 2000 the Geosystems Research Institute is a collaborative of academic scientist, engineers, and government and industry stakeholders active in conducting research to advance knowledge and practice in earth and its systems to improve policy and public awareness. Through the use of geospatial technologies and high-performance computing, GRI is at the forefront in addressing some of today’s most pressing agriculture, water resources, conservation, and wetland issues.
News
TrueView on Campus with GRI: Mississippi State Part 2
September 23, 2022 - Cary ...
MSU Researcher and Faculty Member Named Associate Director of GRI
August 17, 2022 - A ...
Research
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Maritime Domain Awareness
Maritime Domain Awarenes Using Networked Unmanned System with Integrated High Resolution Sensors
Maritime Domain Awarenes Using Networked Unmanned System with Integrated High Resolution Sensors
Airport Wildlife
Airport Wildlife Monitoring Utilizing sUAS
Airport Wildlife Monitoring Utilizing sUAS
Cuban Bulrush
Aquatic Invasive Species: Cuban Bulrush
Aquatic Invasive Species: Cuban Bulrush
MBRACE
Mississippi Based Restore Act Center of Excellence
Mississippi Based Restore Act Center of Excellence
AAR
Advancing Agricultural Research Through High-Performance Computing
Advancing Agricultural Research Through High-Performance Computing
Gull Escape Reactions
Identifying Gull Escape Reactions Using Multiple Object Tracking (MOT) with sUAS Videos
Identifying Gull Escape Reactions Using Multiple Object Tracking (MOT) with sUAS Videos
Marsh Terraces
The Efficacy of Marsh Terraces in Enhancing and Restoring Gulf Coastal Wetlands
The Efficacy of Marsh Terraces in Enhancing and Restoring Gulf Coastal Wetlands
RST
Remote Sensing Toolkit to Incorporate Planetary Data
Remote Sensing Toolkit to Incorporate Planetary Data
Amphibious Ground Vehicles
Amphibious Unmanned Ground Vehicle Sensor System for Rapid Detection of PFAS in Water
Amphibious Unmanned Ground Vehicle Sensor System for Rapid Detection of PFAS in Water
All Projects
Research
Previous
Next
Maritime Domain Awareness
Maritime Domain Awarenes Using Networked Unmanned System with Integrated High Resolution Sensors
Maritime Domain Awarenes Using Networked Unmanned System with Integrated High Resolution Sensors
Airport Wildlife
Airport Wildlife Monitoring Utilizing sUAS
Airport Wildlife Monitoring Utilizing sUAS
Cuban Bulrush
Aquatic Invasive Species: Cuban Bulrush
Aquatic Invasive Species: Cuban Bulrush
MBRACE
Mississippi Based Restore Act Center of Excellence
Mississippi Based Restore Act Center of Excellence
AAR
Advancing Agricultural Research Through High-Performance Computing
Advancing Agricultural Research Through High-Performance Computing
Gull Escape Reactions
Identifying Gull Escape Reactions Using Multiple Object Tracking (MOT) with sUAS Videos
Identifying Gull Escape Reactions Using Multiple Object Tracking (MOT) with sUAS Videos
Partners in Success
Expanding the Intersection between Producer Experimentation and University Research
Expanding the Intersection between Producer Experimentation and University Research
Waterborne Biochemical Agents
Persistent Autonomous Mobile Monitoring of Waterborne Biochemical Agents
Persistent Autonomous Mobile Monitoring of Waterborne Biochemical Agents
RST
Remote Sensing Toolkit to Incorporate Planetary Data
Remote Sensing Toolkit to Incorporate Planetary Data
Amphibious Ground Vehicles
Amphibious Unmanned Ground Vehicle Sensor System for Rapid Detection of PFAS in Water
Amphibious Unmanned Ground Vehicle Sensor System for Rapid Detection of PFAS in Water
All Projects
Recent Publications
Ramamoorthy, P., Samiappan, S., Wubben, M. J., Brooks, J. P., Shrestha, A., Panda, R. M., Reddy, R., & Bheemanahalli, R. (2022). Hyperspectral Reflectance and Machine Learning Approaches for the Detection of Drought and Root Knot Nematode Infestation in Cotton. Remote Sensing. 14, 4021. DOI:https://doi.org/10.3390/rs14164021. [Abstract]Senyurek, V., Farhad, M. M., Gurbuz, A., Kurum, M., & Adeli, A. (2022). Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6843-6855. DOI:10.1109/JSTARS.2022.3197794. [Abstract] [Document Site]
Bheemanahalli, R., Ramamoorthy, P., Poudel, S., Samiappan, S., Wijewardane, N., & Reddy, R. (2022). Effects of Drought and Heat Stresses during Reproductive Stage on Pollen Germination, Yield, and Leaf Reflectance Properties in Maize (Zea Mays L.). Plant Direct. Wiley. 6, e434. [Abstract]
Nabi, M., Senyurek, V., Gurbuz, A., & Kurum, M. (2022). Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay-Doppler Maps. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6876-6881. DOI:10.1109/JSTARS.2022.3196658. [Abstract] [Document Site]