Trinath Mahato works as a Climate tech lead at CEED India, a leading organization in the fields of environmental and social research. With expertise in Geo-informatics, he contributes to various research projects, particularly those focusing on forestry, land issues, agricultural ecosystems monitoring, climate change impacts, carbon potential, and green cover dynamics. He is proficient in programming languages such as R, Google Earth Engine, and Python, and is skilled in using GIS software like ArcGIS, QGIS, ERDAS IMAGINE, SNAP, and ENVI for analyzing and visualizing multi-temporal satellite data to generate insights and solutions.

He holds a Master of Science degree in Geoinformatics from the Central University of Jharkhand, where he completed his dissertation on "Monitoring Tea Plantation during 1990 - 2022 using multi-temporal satellite data in Assam, India" DOI:10.13140/RG.2.2.29625.54887. This project involved applying remote sensing and GIS techniques to assess changes in tea plantation area, productivity, and quality over three decades, and identifying the factors affecting them. Additionally, he has a Post Graduate Diploma in Remote Sensing and GIS from Banaras Hindu University, and a Bachelor of Arts degree in Geography from Kazi Nazrul University. He has earned multiple certifications in spatial analysis, morphometric analysis, and QGIS training from reputable organizations.

Dedicated, resolute, and hardworking, Mahato is eager to push the existing boundaries of knowledge in Geo-informatics. He has a profound interest in learning from current experiments and innovations in the field and leverages his skills for exceptional and better research outcomes.

1. FRP (Fire Radiative Power) in relation with Greening or Browning with Vegetation using remote sensing indices like Normalized Difference Vegetation Index (NDVI) globally, in collaboration with Somnath Bar.

2. Assessing the long-term responsiveness of the Kernel Normalized Difference Vegetation Index (KNDVI) to climatic factors such as precipitation, soil moisture, evapotranspiration, and temperature in the context of Northeast India. This involves using advanced multiscale time–frequency decomposition techniques to understand spatiotemporal dynamics and vegetation-climate interactions.

3. Investigating long-term trends of desertification and land degradation in the Central Region of India, focusing on identifying the key drivers contributing to desertification by leveraging advanced machine learning techniques on the Google Earth Engine (GEE) platform.

4. Studying forest degradation in the Central Region of India, analyzing its underlying causes with a specific focus on forest fires as a major contributing factor, using machine learning approaches on the Google Earth Engine (GEE) platform.

• Advanced Machine Learning Techniques for Geo-Spatial Analysis
• Time-Series Analysis in Climate Data
• Advanced Statistical Methods for Environmental Modelling
• Ecosystem Services Valuation using InVEST

• Ph.D. positions in land degradation, vegetation dynamics, or climate change impacts, where I can further my research expertise and contribute to academic advancements.
• Organizations or institutions working on innovative projects in remote sensing, machine learning, and geo-informatics to develop data-driven solutions for environmental challenges.

Feel free to connect with me for collaborative opportunities, research discussions, or potential positions!

• GIS Softwares: ArcGIS Pro | QGIS
• Cloud Computing & Big Data: Google Earth Engine | Mapbox
• Geospatial & Big Data Analysis: Python | Jupyter Notebook | IDE: VSCode | R Studio
• Statistics & Visualizations: R Studio | Excel
• Environmental Modelling/Prediction: InVEST

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