Prajwol Babu Subedi
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Also affiliated: Pokhara University (2022); Tribhuvan University (2021–2022)
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Biography and Research Information
OverviewAI-generated summary
Prajwol Babu Subedi's research focuses on the application of remote sensing and Geographic Information System (GIS) technologies for environmental assessment and resource management. His work includes mapping land use and land cover dynamics, assessing deforestation and forest degradation rates, and estimating forest biomass. Subedi has investigated the potential for agroforestry and mapped forest fire risk zones, often utilizing high-resolution imagery and machine learning techniques within platforms like Google Earth Engine.
His publications detail methodologies for estimating above-ground forest biomass using various sensor data, including GEDI, and employing machine learning and deep learning approaches. He has also explored vegetation structure and its relationship to carbon stock potential in community-managed forests. Subedi collaborates with researchers from the University of Arkansas at Monticello and the University of Arkansas at Fayetteville.
Metrics
- h-index: 3
- Publications: 11
- Citations: 23
Selected Publications
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Multi-sensor forest aboveground biomass estimation using GEDI, machine learning, and deep learning techniques (2025)
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Estimating Above Ground Forest Biomass Using High-Resolution NAIP Imagery and Deep Learning (2025)
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Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine (2025)
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Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR (2025)
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Estimating Above Ground Forest Biomass Using High-Resolution NAIP Imagery, Machine Learning, and Google Earth Engine (2024)
Collaboration Network
Top Collaborators
- Estimating Above Ground Forest Biomass Using High-Resolution NAIP Imagery, Machine Learning, and Google Earth Engine
- Multi-sensor forest aboveground biomass estimation using GEDI, machine learning, and deep learning techniques
- Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR
- Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine
- Estimating Above Ground Forest Biomass Using High-Resolution NAIP Imagery and Deep Learning
- Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR
- Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine
- Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR
- Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine
- Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR
- Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine
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