2026-10-07, 15:15–15:30 (Europe/Amsterdam), Aula Magna
Semi-natural grasslands are critical ecosystems that provide a range of essential services, with their role as habitats for diverse species being among the most significant. However, over the past century, semi-natural grasslands that once covered vast areas across Europe have largely been transformed into intensively managed agricultural lands, abandoned, or converted into forests. These large-scale land-use changes have led to considerable biodiversity loss, making the conservation and restoration of semi-natural grasslands an important component of sustainable landscape management.
We utilized 97 in-situ herbaceous biomass samples collected during the summer of 2019 from alvar grasslands in Western Estonia, all restored between 2015 and 2019. Samples were collected from 20 × 20 cm plots nested within 2 × 2 m botanical plots. Sentinel-1 and Sentinel-2 imagery from the same period was used, with median band values and derived indices (e.g., NDVI, BSI, SAVI, VH/VV) included as predictors.
Random Forest models were developed using Sentinel-1 and Sentinel-2 spectral bands and derived indices as predictors. Model robustness was evaluated using 5-fold cross-validation. Two approaches for linking field and satellite data were tested: point sampling and a 3 × 3 kernel mean, with point sampling performing slightly better.
The model achieved an RMSE of 98 ± 54 g/m², an MAE of 71 ± 30 g/m², and an R² of 0.32 ± 0.08, reflecting the high spatial variability of semi-natural grasslands. SHAP analysis identified SAVI, NDVI, and the vegetation red edge band B8A as the most important predictors, while Sentinel-1 variables contributed less to model performance.
These results highlight the dominant role of optical data in herbaceous biomass estimation and demonstrate that simple point-based sampling can outperform spatial averaging approaches. The proposed methodology provides a practical and scalable solution for monitoring grassland restoration.
This study explores the use of freely available Sentinel-1 and Sentinel-2 data combined with machine learning for monitoring the restoration of semi-natural alvar grasslands in Estonia. Alvar grasslands present a challenging case due to their high spatial heterogeneity and patchy vegetation structure.
Particular attention is given to how field and satellite data are linked, comparing point-based sampling with a 3 × 3 kernel mean approach. This provides practical insight into how methodological choices influence model performance.
The results highlight the importance of optical data and demonstrate a simple, transferable approach for large-scale monitoring of semi-natural grassland restoration.
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I am a PhD researcher in geoinformatics at the University of Tartu, focusing on the use of remote sensing and machine learning to monitor landscape processes and support ecosystem conservation and restoration.