Multi-sensor satellite imagery reveals spatiotemporal changes in peatland water table after restoration

• Main Authors: Isoaho, Aleksi, Ikkala, Lauri, Päkkilä, Lassi, Marttila, Hannu, Kareksela, Santtu, Räsänen, Aleksi
• Other Authors: orcid:0009-0008-0618-0889, orcid:0000-0002-3629-1837, 4100311110, Luonnonvarakeskus
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier BV
• Subjects: remote sensing; optical; SAR; monitoring; machine learning; hydrology; Northern Finland
• Online Access: https://jukuri.luke.fi/handle/10024/554811

Remote sensing (RS) has been suggested as a tool to spatially monitor the status of peatland ecosystem functioning after restoration. However, there have been only a few studies in which post-restoration hydrological changes have been quantified with RS-based modelling. To address this gap, we developed an approach to assess post-restoration spatiotemporal changes in the peatland water table (WT) with optical (Sentinel-2 and Landsat 7–9) and radar (Sentinel-1) imagery. We tested the approach in eleven northern boreal peatlands (six restored, and five control sites) impacted by forestry drainage in northern Finland using Google Earth Engine cloud computing capabilities. We constructed a random forest regression model with spatiotemporal field-measured WT data as a dependent variable and satellite imagery features as independent variables. To assess the spatiotemporal changes, we constructed representative maps for situations before and after restoration, separately for early summer high-water and midsummer low-water conditions. To further quantify temporal changes during 2015–2023 and to test their statistical significance, we conducted a bootstrap hypothesis test for the areas near the restoration measures and similar areas in the control sites. The regression model had a relatively good fit and explanatory capacity (overall R2 = 0.71, RMSE = 6.01 cm), while there were notable site-specific variations. The WT maps showed that the post-restoration changes were not uniform and concentrated near the restoration measures. The bootstrap test showed that the WT increased more in the restored areas (4.7–8.8 cm) than in the control areas (0.1–5.2 cm). Our results indicate that restoration impact on surface hydrology can be quantified with multi-sensor satellite imagery and a machine learning approach in treeless peatlands.