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

Leonidas Alagialoglou, Ioannis Manakos, Olga Brovkina, Jan Novotný, Anastasios Delopoulos

2025

Abstract

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.

Paper Citation

in Harvard Style

Alagialoglou L., Manakos I., Brovkina O., Novotný J. and Delopoulos A. (2025). Assessment of Fine-Tuned Canopy Height Maps from Satellite Imagery: A Case Study in the Czech Republic. In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM; ISBN 978-989-758-741-2, SciTePress, pages 236-243. DOI: 10.5220/0013475200003935

in Bibtex Style

@conference{gistam25,
author={Leonidas Alagialoglou and Ioannis Manakos and Olga Brovkina and Jan Novotný and Anastasios Delopoulos},
title={Assessment of Fine-Tuned Canopy Height Maps from Satellite Imagery: A Case Study in the Czech Republic},
booktitle={Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM},
year={2025},
pages={236-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013475200003935},
isbn={978-989-758-741-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM
TI - Assessment of Fine-Tuned Canopy Height Maps from Satellite Imagery: A Case Study in the Czech Republic
SN - 978-989-758-741-2
AU - Alagialoglou L.
AU - Manakos I.
AU - Brovkina O.
AU - Novotný J.
AU - Delopoulos A.
PY - 2025
SP - 236
EP - 243
DO - 10.5220/0013475200003935
PB - SciTePress