Isoaho et al. (2025) An automated Google Earth Engine application for detecting the impacted area of treeless boreal peatland restoration – A tool for practitioners and decision-makers

Identification

• Journal: Remote Sensing Applications Society and Environment
• Year: 2025
• Date: 2025-12-13
• Authors: Aleksi Isoaho, Timo P. Pitkänen, Lauri Ikkala, Antti Sallinen, Parvez Rana, Hannu Marttila, Lassi Päkkilä, Aleksi Räsänen
• DOI: 10.1016/j.rsase.2025.101836

Research Groups

• Natural Resources Institute Finland (Luke)
• Geological Survey of Finland (GTK)
• Finnish Environment Institute (Syke)
• Water, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of Oulu
• Geography Research Unit, Faculty of Science, University of Oulu

Short Summary

This study developed a user-friendly Google Earth Engine application to detect the hydrological impact of treeless boreal peatland restoration using optical satellite imagery, finding that Near-Infrared (NIR) and Shortwave Infrared 1 (SWIR1) bands are the most effective indicators.

Objective

• To test which optical satellite variables can be used for detecting hydrological restoration impact in treeless boreal peatlands.
• To develop a user-friendly Google Earth Engine (GEE) application based on the results.
• To demonstrate the usage of the application in practice.

Study Configuration

• Spatial Scale: 24 open peatland restoration sites (mostly aapa mires) across Finland. Statistical analyses used circular 30 meter radius buffers. Satellite imagery resolutions were 10-20 meters (Sentinel-2) and 30 meters (Landsat 8-9, resampled to Sentinel-2 resolution). The GEE application defaults to a 3 kilometer radius buffer for analysis and 30 meter radius buffers for time series plots.
• Temporal Scale: Satellite imagery from the growing seasons of 2014–2024. Pre-restoration images were constructed from data 5 years prior to the first restoration-impact year (excluding 2018), and post-restoration images from data up to 5 years after the restoration impact (excluding 2018). Data was specifically filtered for early summer (1 May – 15 June) to capture peak wetness conditions. Restoration measures were conducted between 2018 and 2023.

Methodology and Data

• Models used: Google Earth Engine (GEE) for cloud computing, data processing, and application development. Statistical analyses included Mann-Whitney U test and Kruskal-Wallis test. Cloud masking utilized CloudScore+ and Scene Layer Classification for Sentinel-2, and Quality Assessment pixels for Landsat 8-9. Datasets were harmonized using empirically developed band-specific coefficients for Finnish open peatlands. A pixel-by-pixel 40th percentile approach was used to construct cloudless and representative satellite image mosaics.
• Data sources: Sentinel-2 (S2) Level 2A reflectance product and Landsat 8–9 (L8–9) Collection 2 Level-2 product. National Land Survey of Finland aerial imagery and open data from the Finnish Environment Institute (peatland drainage status raster) were used for visual interpretation and context.

Main Results

• All tested optical variables showed statistically significant differences between pre- and post-restoration data in successfully restored areas, with restoration generally decreasing reflectance of single bands and vegetation index values, indicating increased wetness.
• Near-Infrared (NIR) and Shortwave Infrared 1 (SWIR1) bands were identified as the most effective indicators for detecting hydrological restoration impact, exhibiting the highest H-statistic values in Kruskal-Wallis tests.
• Single bands generally demonstrated stronger statistical significance in detecting changes compared to spectral indices.
• The developed GEE application, which incorporates NIR and SWIR1, effectively visualizes the location and magnitude of restoration impact, providing false-color images and change maps.
• Demonstrations across four sites confirmed the application's ability to determine if restoration measures successfully increased surface wetness and to map the spatial extent of these impacts, which ranged up to approximately 5 hectares.
• The application also showed potential for detecting rewetting effects from damming and ditch filling in partly treed peatlands, suggesting broader applicability.

Contributions

• Identified Near-Infrared (NIR) and Shortwave Infrared 1 (SWIR1) bands as the most effective optical satellite variables for detecting hydrological restoration impact in treeless boreal peatlands.
• Developed a user-friendly Google Earth Engine (GEE) application, making satellite-based restoration impact assessment accessible to practitioners and decision-makers with limited technical expertise.
• Provided a semi-automated, cost-effective tool that quantifies the spatial extent of restoration impact areas, thereby improving monitoring efficiency and aiding in field assessment planning, particularly in response to increasing monitoring requirements (e.g., EU Nature Restoration Law).
• Empirically developed and incorporated harmonisation coefficients for Sentinel-2 and Landsat 8-9 specifically optimized for Finnish open peatlands.

Funding

• Ministry of Environment, Finland (VN/14352/2022)
• European Union LIFE programme (LIFE22-IPN-FI-Priodiversity LIFE)

Citation

@article{Isoaho2025automated,
  author = {Isoaho, Aleksi and Pitkänen, Timo P. and Ikkala, Lauri and Sallinen, Antti and Rana, Parvez and Marttila, Hannu and Päkkilä, Lassi and Räsänen, Aleksi},
  title = {An automated Google Earth Engine application for detecting the impacted area of treeless boreal peatland restoration – A tool for practitioners and decision-makers},
  journal = {Remote Sensing Applications Society and Environment},
  year = {2025},
  doi = {10.1016/j.rsase.2025.101836},
  url = {https://doi.org/10.1016/j.rsase.2025.101836}
}