Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data

Mikołajczyk, Łukasz; Hawryło, Paweł; Netzel, Paweł; Talaga, Jakub; Zdunek, Nikodem; Socha, Jarosław

doi:10.3390/f16071039

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Open AccessArticle

Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data

by

Łukasz Mikołajczyk

,

Paweł Hawryło

,

Paweł Netzel

,

Jakub Talaga

,

Nikodem Zdunek

and

Jarosław Socha

^*

Department of Forest Resources Management, Faculty of Forestry, University of Agriculture in Krakow, 31-120 Cracow, Poland

^*

Author to whom correspondence should be addressed.

Forests 2025, 16(7), 1039; https://doi.org/10.3390/f16071039

Submission received: 17 May 2025 / Revised: 11 June 2025 / Accepted: 16 June 2025 / Published: 20 June 2025

(This article belongs to the Special Issue Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems)

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Abstract

Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification errors, which modern computational approaches aim to minimize. Convolutional neural networks (CNNs) used to model tabular data have recently gained popularity as a highly efficient classification tool. In the present study, a variation of this method is used to classify satellite multispectral data from the Sentinel-2 mission to distinguish between 18 common Polish tree species. The novel model is trained and tested on data from species-homogeneous forest stands. The data form a multi-seasonal time series and cover five years of observations. The model achieved an overall accuracy of 80% and Cohen Kappa of 0.80 of the raw output and increased to 93% with post-processing procedures. Considering the large number of species classified, this is a promising and encouraging result. The presented results indicate the importance of early vegetation season reflectance data in model training. The spectral bands representing the infrared, red-edge and green wavelengths had the greatest impact on the model.

Keywords: machine learning; CNNs; classification; tree species; Sentinel-2; time series

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MDPI and ACS Style

Mikołajczyk, Ł.; Hawryło, P.; Netzel, P.; Talaga, J.; Zdunek, N.; Socha, J. Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data. Forests 2025, 16, 1039. https://doi.org/10.3390/f16071039

AMA Style

Mikołajczyk Ł, Hawryło P, Netzel P, Talaga J, Zdunek N, Socha J. Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data. Forests. 2025; 16(7):1039. https://doi.org/10.3390/f16071039

Chicago/Turabian Style

Mikołajczyk, Łukasz, Paweł Hawryło, Paweł Netzel, Jakub Talaga, Nikodem Zdunek, and Jarosław Socha. 2025. "Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data" Forests 16, no. 7: 1039. https://doi.org/10.3390/f16071039

APA Style

Mikołajczyk, Ł., Hawryło, P., Netzel, P., Talaga, J., Zdunek, N., & Socha, J. (2025). Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data. Forests, 16(7), 1039. https://doi.org/10.3390/f16071039

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Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data

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