Research Areas
Biography and Research Information
OverviewAI-generated summary
Abdullah Al Saim's research focuses on the application of machine learning and data fusion techniques to environmental monitoring, particularly within Arkansas's forests. His work investigates methods for enhancing tree species mapping, estimating above-ground biomass, and modeling wildfire susceptibility. Al Saim utilizes satellite data, often through platforms like Google Earth Engine, to analyze spatio-temporal trends and improve the accuracy of ecological assessments.
His publications include studies on fusion-based approaches for forest monitoring, machine learning for wildfire susceptibility modeling at the state level, and the use of multisensor data for biomass estimation. He has also explored big data analyses for air pollution trends related to wildfires. Al Saim collaborates with Mohamed H. Aly, with whom he has co-authored multiple publications.
Metrics
- h-index: 4
- Publications: 9
- Citations: 60
Selected Publications
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Machine Learning and Multisensor Data Fusion for Forest above Ground Biomass Estimation in Arkansas (2025)
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Enhancing Tree Species Mapping in Arkansas’ Forests Through Machine Learning and Satellite Data Fusion: A Google Earth Engine–Based Approach (2025)
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Fusion-Based Approaches and Machine Learning Algorithms for Forest Monitoring: A Systematic Review (2025)
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Enhancing Tree Species Mapping in Arkansas' Forests through Machine Learning and Satellite Data Fusion: A Google Earth Engine-Based Approach (2024)
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Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine (2024)
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Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA (2022)
Collaboration Network
Top Collaborators
- Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA
- Enhancing Tree Species Mapping in Arkansas’ Forests Through Machine Learning and Satellite Data Fusion: A Google Earth Engine–Based Approach
- Fusion-Based Approaches and Machine Learning Algorithms for Forest Monitoring: A Systematic Review
- Machine Learning and Multisensor Data Fusion for Forest above Ground Biomass Estimation in Arkansas
- Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine
Showing 5 of 6 shared publications
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