Abstract
Structural heterogeneity strongly influences forest ecological function, yet zone-specific spectral diagnostics remain limited. This study integrated Sentinel-2 imagery (2016, 2020, 2024) with field-observed ecological attributes across Foreground Area Density (FAD)-based structural zones in the Tuchola Forest Biosphere Reserve, Poland. The aim was to evaluate whether open Sentinel-2 vegetation indices can capture ecological variation across structurally distinct forest zones using interpretable machine-learning models. Correlation and cluster analyses of 17 vegetation indices revealed substantial multicollinearity, supporting the selection of a reduced set of spectrally distinct indices for modelling. Extra Trees (ET) and LightGBM (LGBM) produced comparable predictive performance, although ET achieved equal or lower RMSE values in most zone × year combinations and was retained for interpretation. Test-set RMSE remained below one degradation class (0.70–0.96), 2.04 moisture units, 2.35 site-type categories, and 34.4 years for stand age. Permutation importance and partial dependence analyses revealed clear zone-specific spectral-ecological relationships. Rare zones exhibited stronger stress-related spectral responses and greater variability in moisture and stand-age-related patterns, whereas Core zones displayed more stable response surfaces across years. NDRE emerged as the most consistent predictor across ecological attributes, while MCARI, NDMI, and CVI provided complementary information depending on the response variable. By combining FAD-based structural stratification, cluster-driven multicollinearity reduction, and interpretable ensemble learning, this framework provides a reproducible approach for linking spectral traits to ecological gradients across fragmentation contexts and supports open-data monitoring of fragmented forest landscapes.
Similar content being viewed by others
Coping with five mismatches between policy and practice in hemiboreal forest stands and landscapes
Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring
Forest age estimation in northern Arkhangelsk region based on machine learning pipeline on Sentinel-2 and auxiliary data
Acknowledgements
The authors thank Dr. Melaine Aubry Kientz and Prof. Grégoire Vincent for valuable advice and guidance during the internship at the AMAP Laboratory, Montpellier. We also thank the Statistical Analysis Center at Nicolaus Copernicus University for access to their computational cluster and expert statistical feedback. We acknowledge the Bank Danych o Lasach – Lasy Państwowe (Forest Data Bank – State Forests) for providing high-quality forest inventory data collected by professional foresters.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Article processing charges were funded by the YUFE CONNECTS, European Platform for Academic Cooperation of the Nicolaus Copernicus University project.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics statement
This research did not involve any experiments on humans, human data, or live vertebrates/higher invertebrates. All analyses were based on open-access remote sensing datasets and publicly available forest inventory data from the Polish State Forests. Therefore, no ethical approval or informed consent was required.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Supplementary Information 1. (download DOCX )
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Dutt, S., Wojtasik, J., Justeau-Allaire, D. et al. Linking structural forest heterogeneity and ecological processes using Sentinel-2 and FAD-based zoning.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-59330-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-59330-z
Keywords
- Forest structural heterogeneity
- Ecological attributes
- Sentinel-2
- FAD
- Interpretable machine learning
Source: Ecology - nature.com
