Remote Sensing • GIS • Forest Science

Dr. Syed Adnan

Senior GIS and Remote Sensing Expert specialising in ALS/LiDAR, forest inventory and modelling, 3D forest structure, geospatial modelling and environmental applications of remote sensing & GIS.

About

Researcher & GIS / Remote Sensing Expert

Dr. Syed Adnan is a Senior GIS and Remote Sensing Expert based in Finland, specialising in LiDAR/ALS, forest inventory, geospatial analysis, and quantitative forest modelling. He currently works at Oy Arbonaut Ltd., where he contributes to large-scale remote sensing and GIS solutions for forest resource assessment and sustainable forest management.

He holds a D.Sc. in Forest Science from the University of Eastern Finland and an M.S. in Remote Sensing and GIS. Previously, he worked as a postdoctoral researcher at the School of Forest Sciences, University of Eastern Finland, and the Department of Forestry, University of Helsinki. His research and professional work combine remote sensing, 3D geospatial analysis, forest inventory, and data-driven modelling to develop practical methods for forest resource assessment, environmental monitoring, and understanding forest structure.

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D.Sc.Forest Science
M.S.Remote Sensing & GIS
LiDAR3D forest analysis
GISSpatial modelling
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Academic & Professional Profiles

Research

Research Interests

01

Forest Remote Sensing

Airborne LiDAR and other remote sensing data for forest inventory, structural characterisation and monitoring.

02

Forest Modelling

Development of algorithms and modelling approaches for forest resource assessment, prediction and stratification.

03

Environmental GIS

Remote sensing and GIS applications for environmental assessment, land-use planning and monitoring.

Airborne LiDAR / ALSForest Inventory3D Remote Sensing GISForest StructureBiomass Modelling Machine LearningEnvironmental MonitoringSpatial Modelling
Featured research

Two Research Highlights

2021 • Remote Sensing of Environment

Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification

Adnan, S., Maltamo, M., Mehtätalo, L., Ammaturo, R.N.L., Packalen, P., & Valbuena, R. The study develops a mathematical framework for maximum entropy in 3D remote sensing based on the Gini coefficient and applies it to LiDAR-based aboveground biomass modelling in Finnish boreal forests.

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2026 • Theoretical and Applied Climatology

Assessing the impacts of land cover changes on urban heat island effect and evapotranspiration patterns in mountain and plateau regions

Islam, A., Ali, S.M., Kanwal, A., Zaman-ul-Haq, M., Tariq, A., Ali, I., Adnan, S., et al. Research addressing land-cover change, urban heat island effects and evapotranspiration patterns using remote sensing and geospatial analysis.

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Selected work

Publications

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2026

Assessing the impacts of land cover changes on urban heat island effect and evapotranspiration patterns in mountain and plateau regions.

Islam, A., Ali, S.M., Kanwal, A., Zaman-ul-Haq, M, Tariq, A., Ali, Iftikhar., Adnan, S., Faqeih, K. Y., Alamri, S. M., Alamery, E. R. and Bokhari, S. A., & Theoretical and Applied Climatology, 157:607.

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2025

Prediction and mapping of boreal forest fire fuel loads using high-resolution satellite stereo imagery

Gopalakrishnan, R., Korhonen, L., Maltamo, M., Adnan, S., & Packalen, P. International Journal of Remote Sensing, 46(21), 8028–8050.

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2023

Identification and Selection of Suitable Landfill Sites using GIS-based Multi-Criteria Decision Analysis in the Peshawar District, Pakistan

Ali, I., Islam, A., Ali, S.M., & Adnan, S. Waste Management & Research, 41(3), 608–619.

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2022

Optimizing the airborne laser scanning estimation of basal area larger than mean (BALM): an indicator of cohort balance in forests

Adnan, S., Valbuena, R., Kauranne, T., Gopalakrishnan, R., & Maltamo, M. Ecological Indicators, 142, 109162.

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Talks & presentations

Watch Dr. Syed Adnan

PhD / DSc Defence — Improvements in forest structural type assessment using airborne laser scanning

Silvilaser 2019 — Potential of forest structural types detected directly from airborne LiDAR data

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For academic or professional enquiries, you can also visit the About and Research pages above.