Identifying Forest Cover of Abandoned Agricultural Lands in the Central Russian Forest-Steppe Using Various Types of Satellite Data
The work was supported by grant of Russian Science Foundation (Project Number 26-17-00361)
DOI:
https://doi.org/10.52575/2712-7443-2026-50-3-646-658Keywords:
abandoned agricultural lands, forest cover, remote sensing data, machine learning, classification algorithms, Landsat, Sentinel-2Abstract
An analysis of the forest cover of abandoned agricultural lands formed during secondary successions, is necessary for monitoring their condition and providing a comprehensive assessment of changes in landscapes. The paper presents the results of forest cover recognition of abandoned agricultural lands of the Central Russian forest-steppe. They were obtained by classifying Sentinel-2 MSI data synthesized in bands of different spatial resolutions and Landsat-9 OLI data using the Random Forest and Support Vector Machine algorithms. The overall accuracy of the models calculated based on these methods was approximately the same in all studied variants, with a slight excess for SVM-based models. Models using Random Forest and Support Vector Machine computed on Sentinel-2 MSI data synthesized in the highest spatial resolution bands to distinguish between areas of closed forest cover of abandoned agricultural land and areas of no forest cover showed overall accuracies of 0.85 and 0.86. Classification models obtained from Sentinel-2 data with additional shortwave infrared bands but lower spatial resolution showed accuracies of 0.80 and 0.82. The accuracy of similar models obtained based on Landsat-9 OLI data was 0.78 and 0.79. For the models obtained using Random Forest methods based on Sentinel-2 MSI data, the green range spectral reflectance showed the greatest contribution to the separation of the analyzed areas of abandoned agricultural land. For similar models obtained using Landsat-9 OLI data, the greatest contribution to class separation was found for the green and shortwave infrared ranges spectral reflectance.
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