High Resolution Topographic Mapping of Southern California with Optical Imagery

Report for SCEC Award #16147

Investigators: William Barnhart (University of Iowa) Michael Willis (CU Boulder) Rich Briggs (USGS Golden) II. Technical Report Exemplary Figure:

Figure 1: Hillshades of three 2-meter DEMs generated from WorldView-2 stereo imagery. The first highlights the trace of the San Andreas Fault and near-fault tectonic geomorphology near the town of Valyermo. The second highlights the locations of landslides on the flank of Anderson Peak near the San Gorgonio Pass (red arrows). These landslides are not readily apparent in optical imagery. The third highlights the high-quality of these DEMs over urbanized regions, as highlighted by Los Angeles International Airport (LAX). The quality of the results in urban areas was unexpected, and the production of DEMs in these regions is motivated by the capability to image building damage following earthquakes in conjunction with methods such as Damage Proxy Maps [Yun et al., 2012] Introduction

High-resolution, digital topographic data sets, or digital elevation models (DEMs), play an integral role in many active tectonics studies. In southern California, DEMs generated from radar (e.g., the Shuttle Radar Topography Mission (SRTM) 30 m

DEM), DEMs generated from multispectral sensors (e.g. the ASTER 30 m DEM and USGS 10 m DEM), and LiDAR point clouds are the primary existing sources of topographic information that are openly available to researchers through NASA, the USGS, and OpenTopography. Together, these resources enable the identification and quantification of discrete surface offsets, modeling of localized and regional fluvial responses to lateral and vertical fault displacements, and mapping of active fault traces [e.g., Kirby and Whipple, 2001; Sherrod et al., 2004; Frankel et al., 2007; Notebaert et al., 2009]. Increasingly, these data sets are also used as geodetic tools to quantify co-seismic offsets and the spatial distribution of surface faulting resulting from earthquakes through both manual mapping and automated techniques such as image cross correlation [e.g., Nissen et al., 2012; Oskin et al., 2012; Barišin et al., 2015]. There are several well-established limitations of these topographic data sets. First, they are often not acquired within a repeat acquisition framework, making it difficult or impossible to generate time series of surface change. Second, there is an order of magnitude difference in the spatial resolution of satellite-acquired global DEMs such as the SRTM and USGS DEMs, and gridded LiDAR datasets such as the B4 LiDAR. Moreover, LiDAR coverage is often highly spatially limited due to its high cost of acquisition (Figure 1). Thus, repeated acquisition of high-resolution (<5 m/pixel), large footprint DEMs presents a challenge for the study of temporally and spatially varying landscapes.

An increasingly viable means to generate repeated, high-resolution topographic data sets over broad regions is through space-borne, commercial optical sensors (Figure 1). The WorldView 1, 2 and 3, and GEOEYE sensors, each with spatial resolutions of 0.3-0.5 m, regularly acquire land surface imagery in both in-track stereo and off-nadir, non-stereo viewing geometries. DEMs can then be derived from these images using photogrammetry algorithms. Depending on the imaging resolution (0.3 to 0.5 m for WorldView and GEOEYE) and quality of imagery, DEMs can be derived with spatial resolutions of 0.5-5 m. Moreover, this imagery can be provided cost-free to federally-funded researchers, making optical DEMs a cost effective way to generate DEMs, update them periodically, and conduct fundamental and operational research immediately following a large earthquake. Processing Workflow and Image Coverage Our initial efforts in this project focused on:

A. Developing a workflow to process DEMs at 2m resolution from the World View and GEOEYE satellites that utilized open source software;

B. Generating DEMs over the San Gorgonio and Ventura Special Fault Study Areas;

C. Assessing the accuracy of the generated DEMs where the B4 and Earthscope LiDAR surveys overlap our imagery (Figure 2).

Additional to these goals, we were able to expand our processing region to include portions of the southern San Andreas Fault, the San Jacinto Fault, the Los Angeles Basin, and the Ellsinore Fault (Figure 2).

Figure 2: Distribution of processed 2m DEMs, the B4 and Earthscope (ES) LiDAR regions, and currently acquired/processing DEMs (black boxes). DEMs are available upon request from the PI and are being made available publicly through OpenTopography. During this project, we experimented with two open source DEM processing tools: NASA’s Ame’s Stereo Pipeline (ASP) stereogrammetry suite, and the Ohio State University’s Surface Extraction with TIN-based Search-space Minimization (SETSM) [Noh and Howat, 2015]. We found that, while more computationally expensive (~30 computing hours per image granule), SETSM produced a more reliable DEM with fewer spatial artifacts, particularly in urban regions or regions of steep relief. Additionally, SETSM produced suites of intermediate processing products that allow us to restart the DEM processing in order to generate higher spatial resolution products without redoing the entire processing chain. Lastly, SETSM is capable of producing DEMs at the native resolution of the stereo imagery (0.5m for WorldView 1 and 2, 0.3m for WorldView 3). Accordingly, all of our derived products have been processed via SETSM. Additionally, this project took advantage of high-performance computing resources available at the University of Iowa. Under the supervision of PI Barnhart and guidance of PI Willis, two undergraduate researchers have generated the majority of the DEMs using the University of Iowa’s Neon and Argon clusters. Image Quality A final part of this project aimed to assess the quality of the generated DEMs. To do this, we extracted LiDAR elevations from the B4 and Earthscope LiDAR swaths provided by OpenTopography where overlap exists with generated DEMs (Figure 2). Generally, we found that, without conducting any additional image co-registration or corrections (e.g.,

the raw DEMs informed only by ground control points provided in the image metadata), the DEMs generated with SETSM agree exceptionally well with 2m resolution rasterized LiDAR point clouds (Figure 3). In the example shown in Figure 3, the LiDAR and optical DEMs agree within a standard deviation of 1 meter. As part of our ongoing effort to publish documentation of the DEMs, the DEMs will be co-registerd to the LiDAR swaths where overlap occurs, ICESAT point clouds, and the SRTM 30m DEM to reduce horizontal uncertainties. Nonetheless, there are a number of image artifacts present in several of the DEMs (Figure 2). Namely, images acquired in early spring months (April 2016) are incoherent in high mountainous regions over the San Gabriel Mountains due to snow cover. There are likewise gaps in low elevation DEMs due to cloud coverage (Figure 2). Filling these gaps can be achieved through the analysis of additional cloud- and snow-free imagery, which are targets for future analysis. Figure 3: Comparison of a World View Digital Elevation/Surface Model (DSM) to the B4 LiDAR near the western end of the Indio Hills. The scatter plot shows the correlation of elevations between each data set, reflecting a standard deviation of ~1m in the vertical dimension. One particular advantage of the optical DEMs is that they provide excellent detail of engineered structures in urban areas such as the Los Angeles basin and city of Ventura (Figure 1). This provides a valuable data set for several different applications:

a. Mapping fault zone topography in urban areas, such as the existence, height, and distribution of fault scarps;

b. Capability to rapidly evaluate earthquake impacts following future events through the identification of deformed, displaced, or fallen structures;

c. Calibration of other remotely sensed metrics of structural damage, such as that from Damage Proxy Maps [Yun et al., 2012].

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