Febrianti et al. (2025) A New Approach to Peatland Groundwater Level Estimation: Leveraging Remote Sensing and Field Data for Fire Risk Mitigation

Identification

• Journal: Springer proceedings in earth and environmental sciences
• Year: 2025
• Date: 2025-11-16
• Authors: Nur Febrianti, Jalu Tejo Nugroho, Khalifah Insan Nur Rahmi, Nurwita Mustika Sari, Nurhayati Nurhayati, Afriyanni Afriyanni
• DOI: 10.1007/978-981-95-3075-5_11

Research Groups

• Postgraduate Program, Universitas Riau, Pekanbaru, Indonesia
• Regional Research and Innovation Agency Pelalawan Regency, Pelalawan, Riau, Indonesia
• National Research and Innovation Agency (BRIN), Jakarta, Indonesia
• School of Environmental Science, Universitas Indonesia, Depok, Indonesia
• Research and Development Agency of Pekanbaru City, Pekanbaru, Riau, Indonesia

Short Summary

This study developed an accurate groundwater level estimation model for Indonesian peatlands by integrating remote sensing drought indices and field data, demonstrating that maintaining groundwater levels above 66 cm is crucial for mitigating peatland fire risk.

Objective

• To develop a more accurate groundwater level (GWL) estimation model by combining field data with drought indices derived from remote sensing technology (NDWI and VSDI) to support water resource management, drought risk mitigation, and sustainable peatland management in Indonesia.

Study Configuration

• Spatial Scale: Tropical peatland areas in Indonesia (implied by author affiliations and problem context).
• Temporal Scale: Data from March, April, and June 2016 for satellite imagery.

Methodology and Data

• Models used: Statistical model (developed using 12 variables), Akaike Information Criterion with correction (AICc) for model selection, cross-validation for model validation.
• Data sources:
  • Primary: Landsat 8 OLI satellite images (March, April, June 2016).
  • Secondary: 1:50,000 scale topographic map, hotspot data from VIIRS, field measurements of peat depth, water table height, and physical peat properties.
  • Derived: Normalized Difference Water Index (NDWI), Visible and Shortwave Infrared Drought Index (VSDI).

Main Results

• A statistical model incorporating 12 variables (including drainage distance, peat depth, bulk density, fiber content, NDWI, and VSDI) was developed.
• The best model, selected from 1,023 variations, achieved a Root Mean Square Error (RMSE) of 16.1 cm and an R² of 84% during cross-validation.
• The study found that maintaining groundwater levels above 66 cm is crucial for reducing the risk of peatland fires.
• The developed model provides a precise method for monitoring groundwater levels in tropical peatland areas.

Contributions

• Introduces a novel and accurate approach for peatland groundwater level estimation by effectively integrating both remote sensing-derived drought indices and comprehensive field data.
• Provides a critical quantitative threshold (66 cm) for groundwater level maintenance, directly informing fire risk mitigation strategies in tropical peatlands.
• Offers a precise and cost-effective monitoring tool that overcomes the limitations of manual measurements, enhancing sustainable peatland management.

Funding

• Not explicitly stated in the provided paper text.

Citation

@article{Febrianti2025New,
  author = {Febrianti, Nur and Nugroho, Jalu Tejo and Rahmi, Khalifah Insan Nur and Sari, Nurwita Mustika and Nurhayati, Nurhayati and Afriyanni, Afriyanni},
  title = {A New Approach to Peatland Groundwater Level Estimation: Leveraging Remote Sensing and Field Data for Fire Risk Mitigation},
  journal = {Springer proceedings in earth and environmental sciences},
  year = {2025},
  doi = {10.1007/978-981-95-3075-5_11},
  url = {https://doi.org/10.1007/978-981-95-3075-5_11}
}