Post-fire erosion dynamics in the Dim River Basin: a remote sensing and Google Earth Engine approach

Abstract Background Forest fires seriously affect ecosystem balance but can also play a vital role in ecosystems. Remote sensing methods provide significant convenience for determining the effects of fires on ecosystem balance, due to their capacity to precisely detect and analyze changes over large areas. These methods help determine the critical role of forests, especially in challenging terrains, in erosion control. This study aims to investigate the impact of forest fires on soil erosion dynamics using the Revised Universal Soil Loss Equation (RUSLE) within the Google Earth Engine (GEE) platform, to highlight the critical role of forests in erosion control, especially in challenging terrains like the Dim River Basin. For the pre-fire (2020) and post-fire (2022) soil loss estimates, topography, land cover, and local characteristics were considered using the RUSLE model, which uses datasets integrating satellite imagery and precipitation. Results Determined soil loss classes in hectares for 2020 and 2022, reflecting pre- and post-fire erosion estimates for the Dim River Basin. The “very low” class experienced a slight increase. In the dominant class, significant shifts occurred in the “moderate,” “severe,” “very severe,” and “extreme” classes, highlighting the considerable influence that forest fires have on the dynamics of erosion. Post-fire soil losses surge to 11.07 tons ha −1 year −1 from 10.94 tons ha −1 year −1 pre-fire, indicating escalated erosion risk post-wildfires, primarily due to intense precipitation and decreased land cover caused by fire. The 7% rise in the extreme class suggests that erosion was intensified by decreased land cover in rocky areas. Conclusions Results underscore the urgency for immediate post-forest fire erosion control measures, advocating sustainable land-use planning in vulnerable regions and effective erosion control methods. This study provides pivotal insights into forest fire impacts on soil erosion, supporting informed conservation and management strategies. In conclusion, GEE integrated with RUSLE effectively monitors soil erosion dynamics and changes after post-forest disturbance. Cloud-based computing ensures accurate predictions, encouraging collaborative efforts for targeted management strategies vital for ecological balance. GEE’s computational prowess positions it as a valuable tool for understanding and formulating effective post-forest fire strategies.