Combining Methods to Estimate Post-Fire Soil Erosion Using Remote Sensing Data

Argentiero, Ilenia; Ricci, Giovanni Francesco; Elia, Mario; D’Este, Marina; Giannico, Vincenzo; Ronco, Francesco Vito; Gentile, Francesco; Sanesi, Giovanni

doi:10.3390/f12081105

Cited by 7 publications

(8 citation statements)

References 90 publications

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“…The curve number (CN) is another parameter often adapted to simulate the increase in runoff under post-fire conditions. The addition of 5, 10, and 15 to the initial value for low, moderate, and high severity fires, respectively, is widely found in the literature [34,41,63,64]. To accommodate the increase of post-fire runoff peaks and the faster rising limb of such peaks, researchers often reduce Manning's n coefficient.…”

Section: Fire-induced Changes On Hydrological Processesmentioning

confidence: 99%

“…The burn severity can be determined in situ using the available descriptors [8,69,70], remote sensing indices such as Normalized Burn Ratio (NBR, 62), or by a combination of both (Fig 3). In the absence of field assessment, NBR is considered the best available method for catchment-scale assessments [47,63,[71][72][73].…”

Section: Integrating Burn Severity In Modelling Predictionsmentioning

confidence: 99%

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Advances on water quality modeling in burned areas: A review

et al. 2022

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Wildfires are a recurring hazard in forested catchments representing a major threat to water security worldwide. Wildfires impacts on water quality have been thoroughly addressed by the scientific community through field studies, laboratory experiments, and, to a lesser extent, the use of hydrological models. Nonetheless, models are important tools to assess on-site and off-site wildfires impacts and provide the basis for post-fire land management decisions. This study aims to describe the current state of the art of post-fire model adaptation, understanding how wildfires impacts are simulated and the options taken by the modelers in selecting parameters. For this purpose, 42 publications on modeling wildfire impacts on the hydrologic cycle and water quality were retrieved from the SCOPUS database. Most studies simulated post-fire hydrological and erosion response in the first year after the fire, while few assessed nutrients changes and long-term impacts. In addition, most simulations ended at the watershed outlet without considering the fate of pollutants in downstream waterbodies. Ash transport was identified as a major research gap, given the difficulties of its incorporation in the current models’ structure and the high complexity in predicting the heterogeneous ash layer. Including such layer would improve models’ ability to simulate water quality in post-fire conditions, being ash a source of nutrients and contaminants. Model complexity and data limitations influenced the spatial and temporal scale chosen for simulations. Post-fire model adaptations to simulate on-site soil erosion are well established, mainly using empirical equations extensively calibrated in the literature. At the watershed level, however, physical and process-based models are preferred for their ability to simulate more complex burned area characteristics. Future research should focus on the simulation of the ash transport and the development of integrated modelling frameworks, combining watershed and aquatic ecosystem models to link the on and off-site impacts of fires.

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Section: Fire-induced Changes On Hydrological Processesmentioning

confidence: 99%

Section: Integrating Burn Severity In Modelling Predictionsmentioning

confidence: 99%

Advances on water quality modeling in burned areas: A review

et al. 2022

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“…Among the vegetation indices extracted from satellite images, the normalized difference vegetation index (NDVI), computed from the red and near-infrared bands, is a highly utilized indicator and can reflect the recovery of post-fire vegetation. [21][22][23][24][25]. Although the revised universal soil loss equation (RUSLE) is an old empirical model developed based on agricultural lands [26], it has been continuously used to estimate soil erosion in large-scale forest areas by using the NDVI extracted from satellite images to calculate the cover factor with technologies of geospatial information [27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Applicability Comparison of GIS-Based RUSLE and SEMMA for Risk Assessment of Soil Erosion in Wildfire Watersheds

Shin,

Park,

Kim

2024

Remote Sensing

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The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate.

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“…A similar approach, integrating NBR and RUSLE, was employed by Efthimiou et al [21] to map the fire severity and soil erosion susceptibility by the Mati fatal wildfire in Eastern Attica, Greece. Argentiero et al [22] used NDVI and relative differenced NBR to analyse the impact on fire severity on soil erosion. Mallinis et al [23] utilised a combination of ASTER imagery, air photos and Landsat TM imagery to map pre-and post-burn severity in their study modelling post-fire erosion risk from a large and intensive wildland fire at the watershed level in a Mediterranean landscape to prioritise protection and management.…”

Section: Introductionmentioning

confidence: 99%

The Impacts of Burn Severity and Frequency on Erosion in Western Arnhem Land, Australia

Bretreger,

Hancock,

Lowry

et al. 2024

Sensors

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Wildfires are pivotal to the functioning of many ecosystems globally, including the magnitude of surface erosion rates. This study aims to investigate the relationships between surface erosion rates and wildfire intensity in the tropical north savanna of Australia. The occurrence of fires in western Arnhem Land, Northern Territory, Australia was determined with remotely sensed digital datasets as well as analogue erosion measurement methods. Analysis was performed using satellite imagery to quantify burn severity via a monthly delta normalised burn ratio (dNBR). This was compared and correlated against on-ground erosion measurements (erosion pins) for 13 years. The dNBR for each year (up to +0.4) displayed no relationship with subsequent erosion (up to ±4 mm of erosion/deposition per year). Poor correlation was attributed to low fire severity, patchy burning, significant time between fires and erosion-inducing rainfall. Other influences included surface roughness from disturbances from feral pigs and cyclone impacts. The findings here oppose many other studies that have found that fires increase surface erosion. This accentuates the unique ecosystem characteristics and fire regime properties found in the tropical Northern Territory. Scenarios of late dry season fires with high severity were not observed in this study and require more investigations. Ecosystems such as the one examined here require specialised management practices acknowledging the specific ecosystem functions and processes. The methods employed here combine both analogue and digital sensors to improve understandings of a unique environmental system.

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Combining Methods to Estimate Post-Fire Soil Erosion Using Remote Sensing Data

Cited by 7 publications

References 90 publications

Advances on water quality modeling in burned areas: A review

Advances on water quality modeling in burned areas: A review

Applicability Comparison of GIS-Based RUSLE and SEMMA for Risk Assessment of Soil Erosion in Wildfire Watersheds

The Impacts of Burn Severity and Frequency on Erosion in Western Arnhem Land, Australia

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