Mountain watersheds recently burned by wildfire often experience greater amounts of runoff and increased rates of sediment transport relative to similar unburned areas. Given the sedimentation and debris flow threats caused by increases in erosion, more work is needed to better understand the physical mechanisms responsible for the observed increase in sediment transport in burned environments and the time scale over which a heightened geomorphic response can be expected. In this study, we quantified the relative importance of different hillslope erosion mechanisms during two postwildfire rainstorms at a drainage basin in Southern California by combining terrestrial laser scanner‐derived maps of topographic change, field measurements, and numerical modeling of overland flow and sediment transport. Numerous debris flows were initiated by runoff at our study area during a long‐duration storm of relatively modest intensity. Despite the presence of a well‐developed rill network, numerical model results suggest that the majority of eroded hillslope sediment during this long‐duration rainstorm was transported by raindrop‐induced sediment transport processes, highlighting the importance of raindrop‐driven processes in supplying channels with potential debris flow material. We also used the numerical model to explore relationships between postwildfire storm characteristics, vegetation cover, soil infiltration capacity, and the total volume of eroded sediment from a synthetic hillslope for different end‐member erosion regimes. This study adds to our understanding of sediment transport in steep, postwildfire landscapes and shows how data from field monitoring can be combined with numerical modeling of sediment transport to isolate the processes leading to increased erosion in burned areas.

This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A lidar was also used on the largest sUAS and as a mobile scanning system. The quality of each of the seven platforms were compared to actual surface measurements gathered with real-time kinematic (RTK)-GNSS and terrestrial laser scanning. Rigorous field and photogrammetric assessment workflows were designed around a combination of structure-from-motion to align images, Monte Carlo simulations to calculate spatially variable error, object-based image analysis to create objects, and MC32-PM algorithm to calculate vertical differences between two dense point clouds. The precision of the sensors ranged 0.115 m (minimum of 0.11 m for MaRS with Sony A7iii camera and maximum of 0.225 m for Mavic2 Pro). In a heterogenous test location with varying slope and high terrain roughness, only three of the seven mobile platforms performed well (MaRS, Inspire 2, and Phantom 4 Pro). All mobile sensors performed better for the homogenous test location, but the sUAS lidar and mobile lidar contained the most noise. The findings presented herein provide insights into cost–benefit of purchasing various sUAS and sensors and their ability to capture high-definition topography.

Rapid technological advances, sustained funding, and a greater recognition of the value of topographic data have helped develop an increasing archive of topographic data sources. Advances in basic and applied research related to Earth surface changes require researchers to integrate recent high-resolution topography (HRT) data with the legacy datasets. Several technical challenges and data uncertainty issues persist to date when integrating legacy datasets with more recent HRT data. The disparate data sources required to extend the topographic record back in time are often stored in formats that are not readily compatible with more recent HRT data. Legacy data may also contain unknown error or unreported error that make accounting for data uncertainty difficult. There are also cases of known deficiencies in legacy datasets, which can significantly bias results. Finally, scientists are faced with the daunting challenge of definitively deriving the extent to which a landform or landscape has or will continue to change in response natural and/or anthropogenic processes. Here, we examine the question: how do we evaluate and portray data uncertainty from the varied topographic legacy sources and combine this uncertainty with current spatial data collection techniques to detect meaningful topographic changes? We view topographic uncertainty as a stochastic process that takes into consideration spatial and temporal variations from a numerical simulation and physical modeling experiment. The numerical simulation incorporates numerous topographic data sources typically found across a range of legacy data to present high-resolution data, while the physical model focuses on more recent HRT data acquisition techniques. Elevation uncertainties observed from anchor points in the digital terrain models are modeled using “states” in a stochastic estimator. Stochastic estimators trace the temporal evolution of the uncertainties and are natively capable of incorporating sensor measurements observed at various times in history. The geometric relationship between the anchor point and the sensor measurement can be approximated via spatial correlation even when a sensor does not directly observe an anchor point. Findings from a numerical simulation indicate the estimated error coincides with the actual error using certain sensors (Kinematic GNSS, ALS, TLS, and SfM-MVS). Data from 2D imagery and static GNSS did not perform as well at the time the sensor is integrated into estimator largely as a result of the low density of data added from these sources. The estimator provides a history of DEM estimation as well as the uncertainties and cross correlations observed on anchor points. Our work provides preliminary evidence that our approach is valid for integrating legacy data with HRT and warrants further exploration and field validation.