RS & GIS π°οΈ | Land Use Land Cover π | Solving Issues Related Land & Vegetation πΎπΏπ | Climate Change ββοΈπ
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.
For more information please see my profiles
- 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