Surface rupture from the 2019 Ridgecrest earthquake sequence, initially associated with the M-w 6.4 foreshock, occurred on 4 July on a-17 km long, northeast-south-west-oriented, left-lateral zone of faulting. Following the M-w 7.1 mainshock on 5 July (local time), extensive northwest-southeast-oriented, right-lateral faulting was then also mapped along a similar to 50 km long zone of faults, including subparallel splays in several areas. The largest slip was observed in the epicentral area and crossing the dry lakebed of China Lake to the southeast. Surface fault rupture mapping by a large team, reported elsewhere, was used to guide the airborne data acquisition reported here. Rapid rupture mapping allowed for accurate and efficient flight line planning for the high-resolution light detection and ranging (lidar) and aerial photography. Flight line planning trade-offs were considered to allocate the medium (25 pulses per square meter [ppsm]) and high-resolution (80 ppsm) lidar data collection polygons. The National Center for Airborne Laser Mapping acquired the airborne imagery with a Titan multispectral lidar system and Digital Modular Aerial Camera (DiMAC) aerial digital camera, and U.S. Geological Survey acquired Global Positioning System ground control data. This effort required extensive coordination with the Navy as much of the airborne data acquisition occurred within their restricted airspace at the China Lake ranges.

Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration algorithms have not yet been applied to this capture modality. We investigate the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a rough vertical surface and examine the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. Qualitative assessment of the shadow detection results indicates pixel level accuracy, which is indirectly validated by shadow restoration improvements when sub-pixel shadow detection is used in lieu of single pixel detection. The inclusion of TLS intensity in existing shadow restoration algorithms that use regions of matching material in sun and shade exposures was found to have a marginal positive influence on restoring shadow spectrum shape, while a proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by 40% and band correlation with respect to a truth image by 45% compared to existing restoration methods.

In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar) along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada) and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF) that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz). The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality.

We propose combining a forward model based support vector regression and the semianalytical radiative transfer model to determine shallow water characteristics. The derived water depths were compared to both LiDAR derived water depths and field measured water depths. The bathymetry results show that both LiDAR and hyperspectral imagery are unable to retrieve water depth for deeper water (>7 m) due to the water attenuation. Fusion was also performed with the LiDAR bathymetry as a constraint on the hyperspectral imagery; the constraint varies the estimated water characteristics but we were not able to independently assess the performance because no measurements of water column characteristics were available. The retrieved hyperspectral bathymetry yielded a standard deviation of 20 cm when compared to LiDAR bathymetry.

The calibration and accuracy analysis of a novel, low-cost, adaptable mobile laser scanning (MLS) system using a Velodyne HDL-32E laser scanner and an Oxford Technical Solutions Inertial+2 inertial navigation system, is described. First, a static calibration of the laser scanner is discussed. The static calibration is shown to improve the overall relative accuracy of point cloud data from the scanner by approximately 20% over the manufacturer-supplied calibration. Then, the determination of system boresight angles and lever-arm offsets using a planar patch least-squares approach is presented. Finally, the calibrated and boresighted MLS is operated in a backpack mode to acquire multiple data sets in an area that contains dense ground control acquired using static terrestrial laser scanning (TLS) and a high-end, survey-grade MLS. The dense ground control is used to examine several methods of estimating the overall errors of the backpack MLS system. Detailed comparison of the MLS data with the TLS and survey-grade MLS control shows that, despite the system's low cost, it is able to reliably collect point cloud data with greater than 10-cm three-dimensional root-mean-square error accuracy. (C) 2016 American Society of Civil Engineers.

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California's Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Airborne laser scanning ALS is a remote-sensing technique that provides scale-accurate 3D models consisting of dense point clouds with x, y planimetric coordinates and altitude z. Using ALS, very high-resolution VHR digital surface models DSMs have been widely used for commercial and scientific applications since the early 1990s. Although there is widespread usage, there has been little comprehensive investigation of quality control for ALS DSMs in the literature, as most studies have been limited to assessing point-based vertical accuracy. This article is dedicated to investigating the quality of ALS DSMs for different land classes using statistical and visual approaches based on absolute and relative vertical accuracy metrics. Rather than a limited number of ground control points GCP, the model-to-model-based approach is applied and DSMs derived from terrestrial laser scanning TLS point clouds that have around 5 mm absolute and 3 mm relative geolocation accuracy were used as the reference data for comparison. The results demonstrate that in open, grass, and building land classes, the ALS DSMs reached both standard deviation σ and normalized median absolute deviation NMAD of 3–5 cm after the elimination of any systematic biases. This result sufficiently satisfies the vertical accuracy requirements for 1/1000-scale topographic maps determined by National Digital Elevation Program NDEP specifications. In tall vegetation, a higher number of discrepancies larger than 0.5 m exist, reversing the relation between σ and NMAD. These vegetation errors also do not appear to be normally distributed. As an additional investigation, the performance of ALS DEMs under dense high-vegetation areas was assessed. These under-canopy ALS DEMs, created using only classified ground returns, offer both σ and NMAD of 12–14 cm, a performance level that is difficult to achieve under-canopy using photogrammetric techniques.

The capability of airborne LiDAR scanners (ALS) to record returns from the ground surface and other targets occluded by forest canopies has been of great value for geosciences and military operations. In this paper we present preliminary results from efforts aimed to characterize different types of forest canopies and to assess the quantity and quality of potential ground returns obtained through different configurations of small footprint airborne mapping LiDAR systems. The final goal of this work is to provide a methodology that allows for the quantification of the “openness” of a forest canopy and procedures to determine the best configuration of ALS systems that ensures maximum detection of ground returns independent of the many different system designs currently available.

Through discussions between the Federal Emergency Management Agency (FEMA) and the U.S. Geological Survey (USGS) following the South Napa earthquake, it was determined that several key decision points would be faced by FEMA for which additional information should be sought and provided by USGS and its partners. This report addresses the four tasks that were agreed to. These tasks are (1) assessment of ongoing fault movement (called afterslip) especially in the Browns Valley residential neighborhood, (2) assessment of the shaking pattern in the downtown area of the City of Napa, (3) improvement of information on the fault hazards posed by the West Napa Fault System (record of past earthquakes and slip rate, for example), and (4) imagery acquisition and data processing to provide overall geospatial information support to FEMA.

Lidar (light detection and ranging), a method by which the precise time of flight of emitted pulses of laser energy is measured and converted to distance for reflective targets, has helped scientists make topographic maps of Earth's surface at scales as fine as centimeters. These maps have allowed the discovery and analysis of myriad otherwise unstudied features, such as fault scarps, river channels, and even ancient ruins [Glennie et al., 2013b].

We describe a prototype compact mobile laser scanning system that may be operated from a backpack or unmanned aerial vehicle. The system is small, self-contained, relatively inexpensive, and easy to deploy. A description of system components is presented, along with the initial calibration of the multi-sensor platform. The first field tests of the system, both in backpack mode and mounted on a helium balloon for real-world applications are presented. For both field tests, the acquired kinematic LiDAR data are compared with highly accurate static terrestrial laser scanning point clouds. These initial results show that the vertical accuracy of the point cloud for the prototype system is approximately 4 cm (1σ) in balloon mode, and 3 cm (1σ) in backpack mode while horizontal accuracy was approximately 17 cm (1σ) for the balloon tests. Results from selected study areas on the Sacramento River Delta and San Andreas Fault in California demonstrate system performance, deployment agility and flexibility, and potential for operational production of high density and highly accurate point cloud data. Cost and production rate trade-offs place this system in the niche between existing airborne and tripod mounted LiDAR systems.