Peatland wetness as an indicator of fire occurrence in Forest and Land Fires (FLFs) - Prayoga, M. Bayu Rizky; Karuniasa, Mahawan; Frimawaty, Evi (Journal of Earth Kingdom, 2024)
Background: Peatland ecosystems play an important role in the hydrological cycle and carbon cycling. In Indonesia, peatlands store about 28.6 gigatonnes of carbon which is equivalent to 10 years of global fossil fuel emissions. Peatlands act as a water storage during wet seasons and slowly release water during dry seasons to maintain river discharges and hydrological balance. However, climate change induced prolonged drought has increased peatland dryness in recent decades which elevate the risks of unwanted peatland fires. During El Nino-induced drought in 2015, over 2.6 million hectares of forest and land burned, emitting 0.81–1.4 gigatonnes of greenhouse gasses. The extreme fires damaged biodiversity, degraded water quality and displaced thousands of locals. This study aimed to analyze peatland wetness as an indicator of fire occurrences in forest and land fires (FLFs) in Riau, Indonesia by examining the relationship between degree of peatland wetness derived from satellite imagery and hotspots data. Methods: Peatland wetness was estimated from microwave backscattering coefficients at several RadarSat synthetic aperture radar (SAR) wavelengths and cross validated with water table depth measurements from 120 monitoring wells. Hotspots data between 2015-2020 were obtained from NASA's MODIS active fire product. Findings: Preliminary results showed significant negative correlations between peatland wetness and numbers of hotspots in peatlands, with more hotspots occurring in drier peatlands compared to wetter ones. This implies that maintaining peatland hydrological functions through continuous saturation is pivotal to prevent severe peatland wildfires under future climate change. Conclusion: Conservation efforts to restore hydrological balance in degraded peatlands through re-wetting strategies are recommended. Further research utilizing machine learning algorithms to produce high-resolution peatland wetness maps can improve fire risk monitoring in peatlands. Novelty/Originality of this Study: This study introduces the novel concept of utilizing peatland wetness as a key indicator for predicting and mitigating forest and land fires in Indonesia, particularly in Riau Province. By combining peatland moisture and temperature data, the research establishes threshold values to better predict fire risks and guide timely mitigation efforts, thereby enhancing the efficiency and effectiveness of FLF response activities.