Fire Induced Rock Spalls as Long-Term Traps for Ash

Severe fires accelerate rock weathering by spalling and exfoliation, creating abundant peels, flakes or spalls. In the following years, these spalls serve as physical traps which accommodate fine particles of dust, ash, organic matter, etc. We searched for traces of ash trapped under the spalls, after four major fires: 1989, 2005, 2010 and 2015 in Israel. Samples were collected beneath the spalls that formed on the rock outcrops, and in the immediate vicinity above and below them. Three laboratory analyses were performed: pH, EC and color. Five mineral/organic compounds were measured across the Mid-Infrared (MIR) spectral region at diffuse reflectance infrared Fourier transform mode: Hydroxylapatite (HAp), charcoal, organic carbon, montmorillonite and kaolinite. Several statistical analyses were performed: MANOVA, PCA and silhouette analysis on K-means clustering. The results show evidence of ash trapped under the spalls formed during the 2005 and 2010 fires, 6 to 11 years after the fires. Charcoal presence is evident, as well as increased amounts of HAp and organic carbon. In the exposed soil above or below the burned rock outcrop, these values are lower. Negligible amounts of ash were measured 27 years after the fire. In the 2015 burned outcrop, large amounts of charcoal were found above and below the outcrop, but not under the spalls. It seems that on the carbonate slopes of Israel and under Mediterranean climate, the time required for spalls to begin functioning as traps is longer than one rainy season, while ash traces are preserved in these traps for a period of two-three decades

REMOTE SENSING TECHNIQUES TO ASSESS POST-FIRE EFFECTS AT THE HILLSLOPE AND SUB-BASIN SCALES VIA MULTI-SCALE MODEL

Post-fire environmental footprint is expected at varying scales in space and in time and demands development of multi-scale monitoring approaches. In this paper, a spatially and temporally explicit multi-scale model that reveals the physical and morphological indicators affecting hillslope susceptibility at varying scales, is explained and demonstrated. The qualitative and quantitative suitability classification procedures are adapted to translate the large-scale space-borne data supplied by satellite systems (Landsat OLS8 and Sentinel 2 and 3) to local scale produced by a regional airborne survey performed by unmanned aerial vehicle (UAV). At the smallest spatial and temporal resolution, a daily airborne imagery collection by UAV is linked to micro-topography model, using statistical and mathematical approaches