Search

Abstract The frequency, severity, and spatial extent of destructive wildfires have increased in several regions globally over the past decades. While direct impacts from wildfires are devastating, the hazardous legacy of wildfires affects nearby communities long after the flames have been extinguished. Post‐wildfire soil conditions control the persistence, severity, and timing of cascading geohazards in burned landscapes. The interplay and feedback between geohazards and wildfire‐induced changes to soil properties, land cover conditions, and near‐surface and surface processes are still poorly understood. Here, we synthesize wildfire‐induced processes that can affect the critical attributes of burned soils and their conditioning of subsequent geohazards. More specifically, we discuss the state of knowledge pertaining to changes in mineralogical, hydraulic, mechanical, and thermal properties of soil due to wildfire with a focus on advances in the past decade. We identify how these changes in soil properties alter evapotranspiration, interception, sediment transport, infiltration, and runoff. We then link these alterations to the evolution of different geohazards, including dry raveling, erosion, rockfalls, landslides, debris flows, and land subsidence. Finally, we identify research gaps and future directions to advance knowledge on how wildfires control the evolution of various earth surface processes and geohazards over time.

Landslides are a pervasive natural disaster, resulting in severe social, environmental and economic impacts worldwide. The tropical, mountainous landscape in South-West Mexico is predisposed to landslides because of frequent hurricanes and earthquakes. The main goal of this study is to compare landslide susceptibility maps in Guerrero derived using high-resolution LIDAR (light detection and ranging) data from both a manual landslide event inventory and an automated landslide inventorying algorithm. The paper also highlights the importance of applying LIDAR data in landslide inventorying and susceptibility mapping. We mapped landslides based on two approaches: (1) manual mapping using satellite images and (2) automatic identification of landslide morphology employing the Contour Connection Method (CCM). We produced a landslide susceptibility map by computing the probability of landslide occurrence from statistical relationships of inventoried landslides detected with LIDAR digital terrain models (DTMs) and derived landslide-causing factors using the logistic regression method. Our results suggest that the automated inventory derived through the CCM algorithm with LIDAR DTMs effectively minimizes the time-consuming and subjective manual inventorying process. The high overall prediction accuracy (up to 0.83) from logistic regression demonstrates the validity and applicability deriving reliable landslide susceptibility maps from an automated inventory; however, LIDAR data are required.

Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have not been widely adopted by practitioners because of the use of input parameters without physical meaning, a lack of transparency in machine-learning based mapping techniques, and limitations in resulting products, which are not ordinarily designed or tested on a large-scale or in diverse geologic units. To this end, this work presents a new semi-automated method, called the Scarp Identification and Contour Connection Method (SICCM), which adapts to diverse geologic settings automatically or semi-automatically using interventions driven by simple inputs and interpretation from an expert mapper. The applicability of SICCM for use in landslide inventory mapping is demonstrated for three diverse study areas in western Oregon, USA by assessing the utility of the results as a landslide inventory, evaluating the sensitivity of the algorithm to changes in input parameters, and exploring how geology influences the resulting landslide inventory results. In these case studies, accuracies exceed 70%, with reliability and precision of nearly 80%. Conclusions of this work are that (1) SICCM efficiently produces meaningful landslide inventories for large areas as evidenced by mapping 216 km2 of landslide deposits with individual deposits ranging in size from 58 to 1.1 million m2; (2) results are predictable with changes to input parameters, resulting in an intuitive approach; (3) geology does not appear to significantly affect SICCM performance; and (4) the process involves simplifications compared with more complex alternatives from the literature.