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, particularly utilizing satellite data and Google Earth Engine, for environmental monitoring and resource management. His work investigates methods to enhance the mapping of tree species, estimate above-ground biomass, and model wildfire susceptibility, with a specific emphasis on the state of Arkansas. Al Saim has published on these topics, including studies that analyze spatio-temporal trends of air pollution due to wildfires in California. His research network includes collaborators such as Mohamed H. Aly and Mohamed M. Aly from the University of Arkansas at Fayetteville, with whom he has co-authored multiple publications. Al Saim's scholarly output is characterized by an h-index of 4, with 9 publications and 45 citations.
Metrics
- h-index: 4
- Publications: 9
- Citations: 50
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
- Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine
- Machine Learning and Multisensor Data Fusion for Forest above Ground Biomass Estimation in Arkansas
Showing 5 of 6 shared publications
- SPATIO-TEMPORAL TRENDS OF AIR POLLUTION DUE TO WILDFIRES IN CALIFORNIA: INFERRED FROM MODIS MAIAC AND SENTINEL-5P
- MODELING WILDFIRE SUSCEPTIBILITY IN ARKANSAS USING GIS-BASED MULTIPLE REGRESSION AND RANDOM FOREST
- MULTISENSOR SATELLITE DATA FUSION AND MACHINE LEARNING FOR ESTIMATING AND EXTRAPOLATING ABOVE-GROUND BIOMASS IN ARKANSAS FORESTS
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