Estimating leaf area index in semi-Mediterranean oak forests using Sentinel-2 and machine learning algorithms: cross-comparison with global satellite products

doi:10.3832/ifor4798-019

Estimating leaf area index in semi-Mediterranean oak forests using Sentinel-2 and machine learning algorithms: cross-comparison with global satellite products

Naseh Miri⁽¹⁾, Parviz Fatehi⁽¹⁾, Mahtab Pir Bavaghar⁽²⁾ , Lucie Homolová⁽³⁾

iForest - Biogeosciences and Forestry, Volume 19, Issue 6, Pages 388-395 (2026)
doi: https://doi.org/10.3832/ifor4798-019
Published: Nov 06, 2026 - Copyright © 2026 SISEF

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Geo-mapping

Leaf area index (LAI) provides crucial insights into forest canopy structure, vegetation density, and overall ecosystem functioning. In the Zagros Mountains of northwestern Iran, traditional silvopastoral practices rely on tree foliage as animal fodder for local communities. Understanding and estimating LAI in these forests can therefore support the adoption of appropriate strategies for sustainable management. This study aims to map LAI in the Zagros semi-Mediterranean Forests (ZMFs) in Iran using Machine Learning Regression Algorithms (MLRAs) combined with Sentinel-2 (S-2) data and a Digital Elevation Model (DEM). We employed three MLRAs, i.e., Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Random Forest (RF). To train and evaluate the algorithms’ performance, in situ LAI data were collected in 60 field plots measuring 20×20 m. The results revealed that the RF algorithm performed best, with R² = 0.82, RMSE = 0.16 m² m^-2, and rRMSE = 16.84. We further cross-compared the generated LAI map with three satellite-derived LAI products: Sentinel-2 SNAP, MODIS, and PROBA-V. The results indicated that the uncertainty of global LAI products varies from 30% to 55% higher than that of our applied approach. The study concludes that integrating S-2 data with DEM and MLRAs provides an accurate and reliable method for mapping LAI in open-canopy ZMFs. However, global LAI products show higher uncertainty because of lower spatial resolution and the lack of suitable empirical transfer functions for these forest types.

Keywords

Leaf Area Index, Sentinel-2, Digital Hemispherical Photography, Machine Learning Regression Algorithms, Zagros Forest

Authors’ address

(1)

0009-0002-9461-155X
0000-0002-1667-4750
Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj (Iran)

(2)

0000-0002-4649-6705
Department of Forestry, Faculty of Natural Resources, and The Center for Research and Development of Northern Zagros Forestry, University of Kurdistan, Sanandaj (Iran)

(3)

0000-0001-7455-2834
Global Change Research Institute of the Czech Academy of Sciences, Department of Remote Sensing, Belidla 986/4a, 603 00 Brno (Czech Republic)

Corresponding author

Mahtab Pir Bavaghar
mahtab_bavaghar@yahoo.com

Citation

Miri N, Fatehi P, Pir Bavaghar M, Homolová L (2026). Estimating leaf area index in semi-Mediterranean oak forests using Sentinel-2 and machine learning algorithms: cross-comparison with global satellite products. iForest 19: 388-395. - doi: 10.3832/ifor4798-019

Academic Editor

Angelo Nolè

Paper history

Received: Jan 20, 2025
Accepted: Apr 21, 2026

First online: Nov 06, 2026
Publication Date: Dec 31, 2026
Publication Time: 6.63 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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