Integrated approach of RUSLE, GIS and ESA Sentinel-2 satellite data for post-fire soil erosion assessment in Basilicata region (Southern Italy)

Abstract

Fire effects consist not only in direct damage to the vegetation but also in the modification of both chemical and physical soil properties. Fire can affect the alteration of soil properties in different ways depending on fire severity and soil type. The most important consequences concern changes in soil responsiveness to the water action and the subsequent increase in sediment transport and erosion. Post fire soil loss can increase in the first year by several orders of magnitude compared to pre-fire erosion. In this study a distributed model based on the Revised Universal Soil Loss Equation (RUSLE) is used to estimate potential post-fire soil loss for four different fire events occurred in Basilicata region in 2017. Geographic Information System techniques and remote sensing data have been adopted to build a prediction model of post-fire soil erosion risk. Results show that this model is not only able to quantify post-fire soil loss but also to identify the complexity of the relationships between fire severity and all the factors that influence soil susceptibility to erosion.

Comparing the impacts of wildfire and meteorological variability on hydrological and erosion responses in a Mediterranean catchment

“…Besides, burn severity is increasingly being recognised as a decisive factor controlling post‐wildfire hydrological responses and erosion rates (Benavides‐Solorio & MacDonald, 2005; Larsen & MacDonald, 2007; Shakesby & Doerr, 2006; Vieira, Fernández, Vega, & Keizer, 2015); in this context, burn severity describes the degree to which a burnt area has been changed by a fire (Jain & Graham, 2007; Jain, Graham, & Pilliod, 2004; Keeley, 2009; UNOOSA, 2019). Consequently, quantitative empirical relations between burn severity and soil hydraulic properties (Moody et al, 2016), as well as between burn severity and soil erosion rates (Fernández & Vega, 2016b; Lanorte et al, 2019; Vieira et al, 2014) have been established in recent years.…”

Soil Ecosystem Services and Sediment Production: The Basilicata Region Case Study

“…Being representative of the susceptibility to soil erosion, the K-factor is a complex synthesis of a combination of phenomena such as the splash during rainfall and intensive events, runoff along the upper soil layer - including transportability of sediments - and seepage into the soil [ 56 ]. For these reason, factor is a function of the soil structure [ 57 ], the permeability [ 58 ], the total amount of organic matter [ 59 ], the granulometry [ 60 ], the water contents in topsoil [ 61 ], and a number of quantities that vary over time even in response to major upheavals such as fires [ 62 – 64 ]. An accurate estimation thus requires intensive and time-consuming field measurements [ 65 ] that have resulted in a continuous investigation and testing of experimental methods and estimation procedures based on a limited number of soil features [ 66 ].…”

Soil Erosion and Sediment Load Management Strategies for Sustainable Irrigation in Arid Regions

“…Generally, C ranges between 1 and 0; higher values (≤1) are used for less vegetation, thus considering the land as barren whereas lower values (near to zero) indicate very strong cover and well-protected soil. Lanorte et al [39] have reported that many authors have adopted simplified approaches to estimate C, e.g., by using land cover maps and assigning a C value to each class [40] or by applying remote-sensing techniques such as image classification [41,42] and vegetation indices [43]. Various studies [44][45][46] report mathematical functions to calculate C using the normalized difference vegetation index (NDVI), which is positively correlated with the amount of green biomass and indicates differences in green vegetation coverage [45].…”