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The relationship of acquisition systems to automated stereo correlation.

Today a concerted effort is being made to expedite the mapping process through automated correlation of stereo data. Stereo correlation involves the comparison of radiance (brightness) signals or patterns recorded by sensors. Conventionally, two-dimensional area correlation is utilized but this is a rather slow and cumbersome procedure. Digital correlation can be performed in only one dimension where suitable signal patterns exist, and the one-dimensional mode is much faster. Electro-optical (EO) systems, suitable for space use, also have much greater flexibility than film systems. Thus, an EO space system can be designed which will optimize one-dimensional stereo correlation and lead toward the automation of topographic mapping.-from Author

Photogrammetric Engineering and Remote Sensing↗

Multi-temporal RADARSAT-1 and ERS backscattering signatures of coastal wetlands in southeastern Louisiana

Using multi-temporal European Remote-sensing Satellites (ERS-1/-2) and Canadian Radar Satellite (RADARSAT-1) synthetic aperture radar (SAR) data over the Louisiana coastal zone, we characterize seasonal variations of radar backscat-tering according to vegetation type. Our main findings are as follows. First, ERS-1/-2 and RADARSAT-1 require careful radiometric calibration to perform multi-temporal backscattering analysis for wetland mapping. We use SAR backscattering signals from cities for the relative calibration. Second, using seasonally averaged backscattering coefficients from ERS-1/-2 and RADARSAT-1, we can differentiate most forests (bottomland and swamp forests) and marshes (freshwater, intermediate, brackish, and saline marshes) in coastal wetlands. The student t-test results support the usefulness of season-averaged backscatter data for classification. Third, combining SAR backscattering coefficients and an optical-sensor-based normalized difference vegetation index can provide further insight into vegetation type and enhance the separation between forests and marshes. Our study demonstrates that SAR can provide necessary information to characterize coastal wetlands and monitor their changes.

Photogrammetric Engineering and Remote Sensing↗

Hydratools manual version 1.0, documentation for a MATLAB ® -based post-processing package for the Sontek Hydra

The Sediment Transport Instrumentation Group (STG) at the U.S. Geological Survey (USGS) Woods Hole Science Center has a long-standing comitment to providing scientists with high quality oceanographic data. To meet this commitment, STG personnel are vigilant in checking data as well as hardware for signs of instrument malfunction. STG data sets are accompanied by processing histories to detail data processing procedures that may have modified the natural data signal while removing noise from the data. The history also allows the data to be reprocessed in the ligth of new insight into instrument function and moored conditions. This toolbox was compiled to meet these data quality commitments for data generated by Sontek Hydra systems using both ADV and PCADP probes. In the mid 1900's, the USGS Coastal and Marine Program began frequent deployments of Sontek Hydra systems in support of projects in estuaries, coastal, and continental shelf regions nationwide. Hydra data sets are large and complex in structure, and existing processing and editing tools consisted of fragments of MATLAB code written by USGS scientists to satisfy personal research needs. This code did not meet STG quality control criteria. This toolbox permits engineers and scientists to monitor data quality by: 1. processing data with interactive critical review; 2. preserving data quality indicators; 3. preserving minimally processed and partially processed versions of data sets. STG usually deploys ADV and PCADP probes configured as downward looking, mounted on bottom tripods, with the objective of measuring high-resolution near-bed currents. The velocity profiles are recorded with minimal internal data processing. Also recorded are parameters such as temperature, conductivity, optical backscatter, light transmission, and high frequency pressure. Sampling consists of high-frequency bursts(1–10 Hz) bursts of long duration (5–30 minutes) at regular and recurring intervals for a duration of 1 to 6 months. The result is very large data files, often 500 MB per Hydra, per deployment, in Sontek's compressed binary format. This section introduces the Hydratools toolbox and provides information about the history of the system's development. The USGS philosophy regarding data quality is discussed to provide an understating of the motivation for creating the system. General information about the following topics will also be discussed: hardware and software required for the system, basic processing steps, limitations of program usage, and features that are unique to the programs.

Open-File Report↗

Optical satellite data volcano monitoring: a multi-sensor rapid response system

In this chapter, the use of satellite remote sensing to monitor active geological processes is described. Specifically, threats posed by volcanic eruptions are briefly outlined, and essential monitoring requirements are discussed. As an application example, a collaborative, multi-agency operational volcano monitoring system in the north Pacific is highlighted with a focus on the 2007 eruption of Kliuchevskoi volcano, Russia. The data from this system have been used since 2004 to detect the onset of volcanic activity, support the emergency response to large eruptions, and assess the volcanic products produced following the eruption. The overall utility of such integrative assessments is also summarized. The work described in this chapter was originally funded through two National Aeronautics and Space Administration (NASA) Earth System Science research grants that focused on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. A skilled team of volcanologists, geologists, satellite tasking experts, satellite ground system experts, system engineers and software developers collaborated to accomplish the objectives. The first project, Automation of the ASTER Emergency Data Acquisition Protocol for Scientific Analysis, Disaster Monitoring, and Preparedness , established the original collaborative research and monitoring program between the University of Pittsburgh (UP), the Alaska Volcano Observatory (AVO), the NASA Land Processes Distributed Active Archive Center (LP DAAC) at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, and affiliates on the ASTER Science Team at the Jet Propulsion Laboratory (JPL) as well as associates at the Earth Remote Sensing Data Analysis Center (ERSDAC) in Japan. This grant, completed in 2008, also allowed for detailed volcanic analyses and data validation during three separate summer field campaigns to Kamchatka Russia. The second project, Expansion and synergistic use of the ASTER Urgent Request Protocol (URP) for natural disaster monitoring and scientific analysis , has expanded the project to other volcanoes around the world and is in progress through 2011. The focus on ASTER data is due to the suitability of the sensor for natural disaster monitoring and the availability of data. The instrument has several unique facets that make it especially attractive for volcanic observations (Ramsey and Dehn, 2004). Specifically, ASTER routinely collects data at night, it has the ability to generate digital elevation models using stereo imaging, it can collect data in various gain states to minimize data saturation, it has a cross-track pointing capability for faster targeting, and it collects data up to ±85° latitude for better global coverage. As with any optical imaging-based remote sensing, the viewing conditions can negatively impact the data quality. This impact varies across the optical and thermal infrared wavelengths as well as being a function of the specific atmospheric window within a given wavelength region. Water vapor and cloud formation can obscure surface data in the visible and near infrared (VNIR)/shortwave infrared (SWIR) region due mainly to non-selective scattering of the incident photons. In the longer wavelengths of the thermal infrared (TIR), scattering is less of an issue, but heavy cloud cover can still obscure the ground due to atmospheric absorption. Thin clouds can be optically-transparent in the VNIR and TIR regions, but can cause errors in the extracted surface reflectance or derived surface temperatures. In regions prone to heavy cloud cover, optical remote sensing can be improved through increased temporal resolution. As more images are acquired in a given time period the chances of a clear image improve dramatically. The Advanced Very High Resolution Radiometer (AVHRR) routine monitoring, which commonly collects 4-6 images per day of any north Pacific volcano, takes advantage of this fact. The rapid response program described in this chapter also improves the temporal resolution of the ASTER instrument. ASTER has been acquiring images of volcanic eruptions since soon after its launch in December 1999. An early example included the observations of the large pyroclastic flow deposit emplaced at Bezymianny volcano in Kamchatka, Russia. The first images in March 2000, just weeks after the eruption, revealed the extent, composition, and cooling history of this large deposit and of the active lava dome (Ramsey and Dehn, 2004). The initial results from these early datasets spurred interest in using ASTER data for expanded volcano monitoring in the north Pacific. It also gave rise to the multi-year NASA-funded programs of rapid response scheduling and imaging throughout the Aleutian, Kamchatka and Kurile arcs. Since the formal establishment of the programs, the data have provided detailed descriptions of the eruptions of Augustine, Bezymianny, Kliuchevskoi and Sheveluch volcanoes over the past nine years (Wessels et al., in press; Carter et al., 2007, 2008; Ramsey et al., 2008; Rose and Ramsey, 2009). The initial research focus of this rapid response program was specifically on automating the ASTER sensor’s ability for targeted observational scheduling using the expedited data system. This urgent request protocol is one of the unique characteristics of ASTER. It provides a limited number of emergency observations, typically at a much-improved temporal resolution and quicker turnaround with data processing in the United States rather than in Japan. This can speed the reception of the processed data by several days to a week. The ongoing multi-agency research and operational collaboration has been highly successful. AVO serves as the primary source for status information on volcanic activity, working closely with the National Weather Service (NWS), Federal Aviation Administration (FAA), military and other state and federal emergency services. Collaboration with the Russian Institute of Volcanology and Seismology (IVS)/Kamchatka Volcanic Eruption Response Team (KVERT) is also maintained. Once a volcano is identified as having increased thermal output, ASTER is automatically tasked and the volcano is targeted at the next available opportunity. After the data are acquired, scientists at all the agencies have access to the images, with the primary science analysis carried out at the University of Pittsburgh and AVO. Results are disseminated to the responsible monitoring agencies and the global community through e-mail mailing lists.

Book chapter↗

An evaluation of gap-filled Landsat SLC-off imagery for wildland fire burn severity mapping

n May 31, 2003 unusual artifacts appeared within image data collected by the Enhanced Thematic Mapper plus (ETM+) instrument on-board the Landsat 7 spacecraft. The U.S. Geological Survey (USGS), with the support of NASA, has been working to find a means of compensating for the data gaps that result from a failure of the instrument’s scan line corrector (SLC). The SLC is an electromechanical device that compensates for the forward motion of the spacecraft by modifying the instrument’s optical path. The problem is likely due to a mechanical failure of the device for which there is no redundancy and that cannot be repaired or coaxed back into service. Further information regarding Landsat 7 and the SLC failure can be found at the Landsat Project home page (http://landsat7.usgs.gov).

Photogrammetric Engineering and Remote Sensing↗

DSWEmod - The production of high-frequency surface water map composites from daily MODIS images

Optical satellite imagery is commonly used for monitoring surface water dynamics, but clouds and cloud shadows present challenges in assembling complete water time series. To test whether the daily revisit rate of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery can reduce cloud obstruction and improve high-frequency surface water mapping, we compared map results derived from Landsat (30-m) and MODIS (250-m) data across the state of California for 2003–2019. We adapted the Dynamic Surface Water Extent (DSWE) model in Google Earth Engine to generate surface water map composites from MODIS imagery every 5, 10, 15, and 30 days, and compared products to monthly Landsat-based DSWE maps. Results for DSWEmod (DSWE MODIS) in California suggest that more than 5% data loss (cloud obstruction, etc.) was present in only 2% of the 15-day time series, as compared to 32% of the monthly Landsat DSWE time series. The five-day DSWEmod composites averaged 8.4% obscuration in the winter months. Area estimates derived from cloud-filtered MODIS and Landsat monthly products have the highest linear correlations compared to streamgage discharge records, suggesting that monthly scale analyses best explain the relationship between surface water area and general streamflow dynamics. Shorter-interval DSWEmod products have lower correlations but utility for understanding the timing of surface water peaks and past flood events.

Journal of the American Water Resources Associatio↗

User guide for the USGS aerial camera Report of Calibration.

Calibration and testing of aerial mapping cameras includes the measurement of optical constants and the check for proper functioning of a number of complicated mechanical and electrical parts. For this purpose the US Geological Survey performs an operational type photographic calibration. This paper is not strictly a scientific paper but rather a 'user guide' to the USGS Report of Calibration of an aerial mapping camera for compliance with both Federal and State mapping specifications. -Author

Photogrammetric Engineering and Remote Sensing↗

USGS aerial resolution targets.

It is necessary to measure the achievable resolution of any airborne sensor that is to be used for metric purposes. Laboratory calibration facilities may be inadequate or inappropriate for determining the resolution of non-photographic sensors such as optical-mechanical scanners, television imaging tubes, and linear arrays. However, large target arrays imaged in the field can be used in testing such systems. The USGS has constructed an array of resolution targets in order to permit field testing of a variety of airborne sensing systems. The target array permits any interested organization with an airborne sensing system to accurately determine the operational resolution of its system. -from Author

Photogrammetric Engineering and Remote Sensing↗

Elevation of the March-April 2010 flood high water in selected river reaches in Rhode Island

A series of widespread, large, low-pressure systems in southern New England in late February through late March 2010 resulted in record, or near record, rainfall and runoff. The total rainfall in the region during this period ranged from about 19 to 25 inches, which coupled with seasonal low evaporation, resulted in record or near record peak flows at 21 of 25 streamgages in Rhode Island and southeastern Massachusetts. The highest record peaks occurred in late March-early April and generally greatly exceeded the earlier March peaks that were near or exceeded the peak of record for 10 of the 25 streamgages. Determination of the flood-peak high-water elevation is a critical part of the recovery operations and post-flood analysis for improving future flood-hazard maps and flood-management practices. High-water marks (HWMs) were identified by the U.S. Geological Survey (USGS) from April 2-7, 2010, and by the U.S. Army Corps of Engineers (USACE) from April 3-7, 2010, in five major river basins including the Blackstone, Hunt, Moshassuck, Pawtuxet, and Woonasquatucket along the mainstems and in many tributaries. The USGS identified 276 HWMs at 137 sites. A site may have more than one HWM, typically upstream and downstream of a bridge. The USACE identified 144 HWMs at 127 sites. The HWMs identified by the USGS and USACE covered about 170 river miles, determined from the upstream and downstream HWMs. Elevation of HWMs were later determined to a standard vertical datum (NAVD 88) using the Global Navigation Satellite System and survey-grade Global Positioning System (GPS) receivers along with standard optical surveying equipment.

Open-File Report↗

Photography and imagery: a clarification of terms

The increased use of pictorial displays of data in the fields of photogrammetry and photo interpretation has led to some confusion of terms, not so much b photogrammetrists as bu users and interpreters of pictorial data. The terms "remote sensing" and "remote sensing of environment" are being used as general terms to describe "the measurement of some property of an object without having the measuring device physically in contact with the object" (Parker, 1962). Measurements of size and shape by photogrammetric and optical means are common examples of remote sensing and therefore require no elaboration. Other techniques of remote sensing of electromagnetic radiation in and beyond the limits of the visible spectrum require some explanation and differentiation from the techniques used in the visible spectrum. The following definitions of "photography" and "imagery" are proposed to clarify these two terms in hope that this will lead to more precise understanding and explanation of the processes.

Photogrammetric Engineering↗

Sediment data collected in 2010 from Cat Island, Mississippi

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, in collaboration with the U.S. Army Corps of Engineers, conducted geophysical and sedimentological surveys in 2010 around Cat Island, Mississippi, which is the westernmost island in the Mississippi-Alabama barrier island chain. The objective of the study was to understand the geologic evolution of Cat Island relative to other barrier islands in the northern Gulf of Mexico by identifying relationships between the geologic history, present day morphology, and sediment distribution. This data series serves as an archive of terrestrial and marine sediment vibracores collected August 4-6 and October 20-22, 2010, respectively. Geographic information system data products include marine and terrestrial core locations and 2007 shoreline data. Additional files include marine and terrestrial core description logs, core photos, results of sediment grain-size analyses, optically stimulated luminescence dating and carbon-14 dating locations and results, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.

Mississippi↗

Evaluation of nearshore bathymetric inversion algorithms using camera observations and synthetic numerical input of surface waves during storms

Nearshore bathymetry is difficult to measure using survey methods when wave heights approach the breaking limit. Remote sensing using digital cameras offers a way to observe the surf zone and calculate water depths based on phase speed but comes with its challenges of potentially noisy data that can introduce error into estimates of frequency and wavenumber used in phase speed calculation. This study investigates the robustness of a new version of a bathymetric inversion algorithm (cBathy, version 2.0) in moderate to energetic wave conditions by comparing depth estimates from timeseries’ of pixel intensity with depth estimates from synthetic water level data. The synthetic data are generated by the phase-resolving numerical model, SWASH, and optical data were collected during a field experiment in 2015. Model results from SWASH computed with known bathymetry are used as input to cBathy, and depth estimates are compared to nearshore surveys. Argus camera observations are also used as input to cBathy for the same times as the SWASH simulations. The SWASH simulations resolve breaking waves and do not include (optical) changes to the relation between water surface slope and pixel intensity, termed modulation transfer function, that occur during wave breaking, enabling better estimates from bathymetric inversion algorithms near morphologic features like sand bars. The results indicate that improvements result from eliminating of disruptions to the modulation transfer function caused by wave breaking and residual foam. We show that the use of synthetic wave data is a valuable means of isolating errors in bathymetric inversion algorithms.

North Carolina↗

Relative performance of several inexpensive accelerometers

We examined the performance of several low-cost accelerometers for highly cost-driven applications in recording earthquake strong motion. We anticipate applications for such sensors in providing the lifeline and emergency-response communities with an immediate, comprehensive picture of the extent and characteristics of likely damage. We also foresee their use as 'filler' instruments sited between research-grade instruments to provide spatially detailed and near-field records of large earthquakes (on the order of 1000 stations at 600-m intervals in San Fernando Valley, population 1.2 million, for example). The latter applications would provide greatly improved attenuation relationships for building codes and design, the first examples of mainshock information (that is, potentially nonlinear regime) for microzonation, and a suite of records for structural engineers. We also foresee possible applications in monitoring structural inter-story drift during earthquakes, possibly leading to local and remote alarm functions as well as design criteria. This effort appears to be the first of its type at the USGS. It is spurred by rapid advances in sensor technology and the recognition of potential non-classical applications. In this report, we estimate sensor noise spectra, relative transfer functions and cross-axis sensitivity of six inexpensive sensors. We tested three micromachined ('silicon-chip') sensors in addition to classical force-balance and piezoelectric examples. This sample of devices is meant to be representative, not comprehensive. Sensor noise spectra were estimated by recording system output with the sensor mounted on a pneumatically supported 545-kg optical-bench isolation table. This isolation table appears to limit ground motion to below our system noise level. These noise estimates include noise introduced by signal-conditioning circuitry, the analog-to-digital converter (ADC), and noise induced in connecting wiring by ambient electromagnetic fields in our suburban laboratory. These latter sources are believed to dominate sensor noise in the quieter sensors we tested. Transfer functions were obtained relative to a research grade force-balance accelerometer (a Kinemetrics TM FBA-11) by shaking the sensors simultaneously on the same shake table and taking spectral ratios with the output of the FBA- 11. This reference sensor is said to have 120 db dynamic range (-+20 bits, though we only digitized it at 16 bits resolution and drove it with relatively small signals). We did not test temperature sensitivity, which is thought to be a significant issue at least for the silicon devices. Though these tests were not designed to be definitive (our anticipated applications do not demand research-grade precision), our tests do appear to have been successful in estimating relative transfer functions from about 0.3 to 50 Hz. Most sensors performed adequately in this range, with essentially fiat relative transfer functions. Noise tests appear to measure sensor noise well for the noisier (generally less expensive) instruments from about 0.1 to 50 Hz.

Open-File Report↗

A comparison of near-bed acoustic backscatter and laser diffraction measurements of suspended sediments

As part of the U.S. Office of Naval Research (ONR, Arlington, VA) mine burial program, an experiment was conducted off the pier at Santa Cruz, CA, to measure the near-bed suspended sediment reference concentration under waves and currents. Two tripods were deployed to carry out the measurements; one consisting mainly of acoustical instrumentation and the other solely of optical instruments. The tripods were located within 15 m of one another on a sandy bed and measurements of the suspended sediment were made using acoustics and optics. Although the experiment was not primarily designed to conduct an intercomparison of acoustical and optical measurements, it was considered interesting to take advantage of the situation and to examine if these two techniques gave comparable results. In particular, measurements of particle size and concentration, obtained using a triple frequency acoustic backscatter system (ABS) have been compared with the commercially available laser miniature scattering and transmissometry instrument (MSCAT). It was found that the mean grain size estimated by the two methods was consistent; however, in contrast, the concentration time series showed differences, both in magnitude and form.

IEEE Journal of Oceanic Engineering↗

InSAR imaging of volcanic deformation over cloud-prone areas - Aleutian islands

Interferometric synthetic aperture radar (INSAR) is capable of measuring ground-surface deformation with centimeter-tosubcentimeter precision and spatial resolution of tens-of meters over a relatively large region. With its global coverage and all-weather imaging capability, INSAR is an important technique for measuring ground-surface deformation of volcanoes over cloud-prone and rainy regions such as the Aleutian Islands, where only less than 5 percent of optical imagery is usable due to inclement weather conditions. The spatial distribution of surface deformation data, derived from INSAR images, enables the construction of detailed mechanical models to enhance the study of magmatic processes. This paper reviews the basics of INSAR for volcanic deformation mapping and the INSAR studies of ten Aleutian volcanoes associated with both eruptive and noneruptive activity. These studies demonstrate that all-weather INSAR imaging can improve our understanding of how the Aleutian volcanoes work and enhance our capability to predict future eruptions and associated hazards.

Photogrammetric Engineering and Remote Sensing↗

The influences of land use and land cover on climate; an analysis of the Washington-Baltimore area that couples remote sensing with numerical simulation

The Sun drives the atmospheric heat engine by warming the terrestrial surface which in turn warms the atmosphere above. Climate, therefore, is significantly controlled by complex interaction of energy flows near and at the terrestrial surface. When man alters this delicate energy balance by his use of the land, he may alter his climatic environment as well. Land use climatology has emerged as a discipline in which these energy interactions are studied; first, by viewing the spatial distributions of their surface manifestations, and second, by analyzing the energy exchange processes involved. Two new tools for accomplishing this study are presented: one that can interpret surface energy exchange processes from space, and another that can simulate the complex of energy transfers by a numerical simulation model. Use of a satellite-borne multispectral scanner as an imaging radiometer was made feasible by devising a gray-window model that corrects measurements made in space for the effects of the atmosphere in the optical path. The simulation model is a combination of mathematical models of energy transfer processes at or near the surface. Integration of these two analytical approaches was applied to the Washington-Baltimore area to coincide with the August 5, 1973, Skylab 3 overpass which provided data for constructing maps of the energy characteristics of the Earth's surface. The use of the two techniques provides insights into the relationship of climate to land use and land cover and in predicting alterations of climate that may result from alterations of the land surface.

Professional Paper↗

Experimental Advanced Airborne Research Lidar (EAARL) Data Processing Manual

The Experimental Advanced Airborne Research Lidar (EAARL) is an example of a Light Detection and Ranging (Lidar) system that utilizes a blue-green wavelength (532 nanometers) to determine the distance to an object. The distance is determined by recording the travel time of a transmitted pulse at the speed of light (fig. 1). This system uses raster laser scanning with full-waveform (multi-peak) resolving capabilities to measure submerged topography and adjacent coastal land elevations simultaneously (Nayegandhi and others, 2009). This document reviews procedures for the post-processing of EAARL data using the custom-built Airborne Lidar Processing System (ALPS). ALPS software was developed in an open-source programming environment operated on a Linux platform. It has the ability to combine the laser return backscatter digitized at 1-nanosecond intervals with aircraft positioning information. This solution enables the exploration and processing of the EAARL data in an interactive or batch mode. ALPS also includes modules for the creation of bare earth, canopy-top, and submerged topography Digital Elevation Models (DEMs). The EAARL system uses an Earth-centered coordinate and reference system that removes the necessity to reference submerged topography data relative to water level or tide gages (Nayegandhi and others, 2006). The EAARL system can be mounted in an array of small twin-engine aircraft that operate at 300 meters above ground level (AGL) at a speed of 60 meters per second (117 knots). While other systems strive to maximize operational depth limits, EAARL has a narrow transmit beam and receiver field of view (1.5 to 2 milliradians), which improves the depth-measurement accuracy in shallow, clear water but limits the maximum depth to about 1.5 Secchi disk depth (~20 meters) in clear water. The laser transmitter [Continuum EPO-5000 yttrium aluminum garnet (YAG)] produces up to 5,000 short-duration (1.2 nanosecond), low-power (70 microjoules) pulses each second. Each pulse is focused into an illumination area that has a radius of about 20 centimeters on the ground. The pulse-repetition frequency of the EAARL transmitter varies along each across-track scan to produce equal cross-track sample spacing and near uniform density (Nayegandhi and others, 2006). Targets can have varying physical and optical characteristics that cause extreme fluctuations in laser backscatter complexity and signal strength. To accommodate this dynamic range, EAARL has the real-time ability to detect, capture, and automatically adapt to each laser return backscatter. The backscattered energy is collected by an array of four high-speed waveform digitizers connected to an array of four sub-nanosecond photodetectors. Each of the four photodetectors receives a finite range of the returning laser backscatter photons. The most sensitive channel receives 90% of the photons, the least sensitive receives 0.9%, and the middle channel receives 9% (Wright and Brock, 2002). The fourth channel is available for detection but is not currently being utilized. All four channels are digitized simultaneously into 65,536 samples for every laser pulse. Receiver optics consists of a 15-centimeter-diameter dielectric-coated Newtonian telescope, a computer-driven raster scanning mirror oscillating at 12.5 hertz (25 rasters per second), and an array of sub-nanosecond photodetectors. The signal emitted by the pulsed laser transmitter is amplified as backscatter by the optical telescope receiver. The photomultiplier tube (PMT) then converts the optical energy into electrical impulses (Nayegandhi and others, 2006). In addition to the full-waveform resolving laser, the EAARL sensor suite includes a down-looking 70-centimeter-resolution Red-Green-Blue (RGB) digital network camera, a high-resolution color infrared (CIR) multispectral camera (14-centimeter-resolution), two precision dual-frequency kinematic carrier-phase global positioning system (GPS) receivers, and an

Open-File Report↗

Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools

Large-magnitude avalanches often alter vegetation composition, avalanche path dimensions, and subsequent avalanche return periods. Understanding temporal changes in individual avalanche path trimlines, runout zones, and geomorphic characteristics helps forecasters, planners, and engineers estimate potential avalanche destructive size and impact on infrastructure or settlements in the runout zone. Understanding these changes on a large scale also provides information on post-cycle avalanche distribution. Here, we use remote sensing platforms and change detection techniques to examine vegetation change in avalanche paths in Montana and Colorado. In northwest Montana, we implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. In Colorado, we investigated changes in avalanche path vegetation characteristics prior to and after a widespread large-magnitude avalanche cycle. The highest frequency of avalanche return periods was broadly characterized by grassland and shrubland, but topography greatly influences vegetation classes and return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. We used optical sensors from satellite imagery to analyze changes in Normalized Difference Vegetation Index (NDVI) to calculate ground cover change over time. NDVI, a measure of near-infrared and red bands within the imagery, allowed us to distinguish between green vegetation (e.g., trees and shrubs) and non-vegetated ground cover (e.g., dead and downed trees, rocks, and dirt) within avalanche paths. For this study, we calculated changes in NDVI values by comparing imagery from 2018 to imagery from 2019 after a widespread large magnitude avalanche cycle occurred in March 2019 in Colorado, United States. We applied a filtering process to reduce error, classified NDVI change based on the value distribution, and then calculated area change of all areas within each avalanche path. We completed this process for 1633 avalanche paths throughout Colorado. We found that using NDVI difference values pre- and post-avalanche cycle allowed us to identify ground cover change in avalanche paths throughout Colorado. These changes span from a slight expansion of existing avalanche paths to substantial landscape disturbance. For example, a size D5 avalanche caused severe ground cover change in 18% of one single path near Aspen, Colorado. This suggests that large magnitude avalanches can redefine avalanche path dimensions and could impact subsequent avalanche size and frequency. Using NDVI from satellite imagery is a simple way to detect ground cover changes in avalanche paths on a large scale or in remote areas. In general, remote sensing products to detect and examine vegetation and ground cover change in avalanche paths can help inform avalanche distribution and benefit planning efforts.

Montana↗