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Affiliation

Department of Remote Sensing, Global Change Research Institute of the Czech Academy of Sciences, CzechGlobe, Brno, Czech Republic

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1 Papers

Assessment of Fine-Tuned Canopy Height Maps from Satellite Imagery: A Case Study in the Czech Republic

Topics: Earth Observation and Satellite Data; Computer Vision Applications; Geographic Information Retrieval; Natural Resource Management; Image Processing and Pattern Recognition; Remote Sensing in Climate Change Studies; Deep Learning for Urban and Environment Planning; Machine Learning for Spatial Data; Geostatistics; Forest Management and Climate-smart Forestry

Authors: Leonidas Alagialoglou ¹ , Ioannis Manakos ² , Olga Brovkina ³ , Jan Novotný ³ and Anastasios Delopoulos ¹

Affiliations: ¹ Multimedia Understanding Group, Aristotle University of Thessaloniki, Greece , ² Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece , ³ Department of Remote Sensing, Global Change Research Institute of the Czech Academy of Sciences, CzechGlobe, Brno, Czech Republic

Digital Object Identifier: 10.5220/0013475200003935

Proceedings of GISTAM 2025, ISBN: 978-989-758-741-2

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This study evaluates the performance of a lightweight convolutional Long Short-Term Memory (ConvLSTM)based deep learning model for estimating canopy height across three test areas in the Czech Republic using Sentinel-2 time series data. The model, initially trained on forest data from Germany and Switzerland, incorporate uncertainty quantification techniques and was fine-tuned and evaluated using dense airborne laser scanning (ALS) data collected between 2022 and 2024. Results show that fine-tuning reduced mean absolute error (MAE) from 4.26 m to 2.74 m in the primary test area, with similar improvements across other regions. Species-specific uncertainties were also analyzed, highlighting performance variations between deciduous and coniferous forests.